Nature of the English Language Essay

Introduction, george orwell’s analysis, comparisons with contemporary media excerpts, works cited.

Language is an important tool of communication because it allows people to understand the meaning, urgency and context of messages. George Orwell believes that political and economic issues transform the nature of the English language; moreover, this perception is supported by various media excerpts that portray how capitalism changes communication.

The author argues that language has been abused by poor habits of conceiving the truth. It has been misused to advance individual’s interests and thus abuse the rights of the majority in the society. Secondly, he blames modernity as the chief cause of the collapse of this language and believes that it must suffer the dangers of these changes (Orwell 1).

Orwell admits that language grows depending on how the society changes and thus it cannot be shaped for an individual’s interest. He believes that the decline of the English language is caused by political and economic issues and not the influence of writers. The author believes that when people’s thoughts are corrupt this will affect how they use language (Orwell 1).

However, he is optimistic that this language can be improved if people decide to avoid bad habits like imitating other people’s communication. He recommends that political regeneration will help people to think clearly and get rid of bad communication habits (Orwell 1).

This ensures the fight against bad English is not left to professional writers. Orwell claims that political speeches are aimed at defending issues that are evident and cannot be covered. Therefore, politicians use language to manipulate their followers and create bad impressions of their opponents.

They misuse vocabularies to describe the weaknesses of their opponents. He believes that politics has affected communication by trying to be verbose, so that it can hide injustices using vague vocabularies and phrases. He uses political events like the Japan bomb attacks, Russian purges and deportations, and British rule in India to explain how euphemistic, vagueness and rhetoric questions are used to cover the activities of dictatorial regimes (Orwell 1).

Lastly, he argues that insincerity is the greatest enemy of the English language because people use exhausted idioms and long words to cover the gap between their real and declared interests in various issues. Politicians have used language to mislead their followers. This has led to abuse of language because vocabularies are used out of their contexts (Orwell 1).

He notes that the sloppy use of language becomes a habit when there is poor political environment and this affects language. Dictatorship has transformed the Italian, German and Russian languages to meet the needs of political rulers. Therefore, this has spread bad thoughts among populations and manipulated their actions. He recommends that people should simplify their English to free themselves from follies of orthodoxy.

The media excerpts support Orwell’s arguments and use different speech extracts to sustain their claims (Mariner 2). John le Carre’s novel describes how torture was unleashed in the CIA’s detention camps. George W. Bush gave a vague and euphemist speech regarding the activities that took place in those camps. He did not mention any interrogation procedure used to get information from detainees.

The administration used language to concede how torture was used to force them to cooperate with the CIA officials. The speech highlighted the need for using torture to fight for the cause of humanity and struggle for freedom and liberty. The Bush administration changed the definition of torture to refer to interrogation methods that caused organ failure or death; therefore, this justified the CIA’s use of other painful questioning procedures (Mariner 2).

This author believes that the English language is in a bad state and faces serious challenges that affect communication. He presents that this problem cannot be reduced by deliberate actions because it occurs as a result of civilization. The media excerpts support Orwell’s claims that the use of painful interrogation methods is torture and calling it another name is a lie. It is true that the English language has become ugly and inaccurate because people are irrational.

Mariner, Joanne. Torture by a Different Name. Washington D.C.: Catherine Weymouth, 2006. Print.

Orwell, George and Jeremy Paxman. Shooting an Elephant. London: Penguin Modern Classics, 2009. Print.

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The power of language: How words shape people, culture

Speaking, writing and reading are integral to everyday life, where language is the primary tool for expression and communication. Studying how people use language – what words and phrases they unconsciously choose and combine – can help us better understand ourselves and why we behave the way we do.

Linguistics scholars seek to determine what is unique and universal about the language we use, how it is acquired and the ways it changes over time. They consider language as a cultural, social and psychological phenomenon.

“Understanding why and how languages differ tells about the range of what is human,” said Dan Jurafsky , the Jackson Eli Reynolds Professor in Humanities and chair of the Department of Linguistics in the School of Humanities and Sciences at Stanford . “Discovering what’s universal about languages can help us understand the core of our humanity.”

The stories below represent some of the ways linguists have investigated many aspects of language, including its semantics and syntax, phonetics and phonology, and its social, psychological and computational aspects.

Understanding stereotypes

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Language can play a big role in how we and others perceive the world, and linguists work to discover what words and phrases can influence us, unknowingly.

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How other languages inform our own

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Ralph Waldo Emerson

Chapter IV from Nature , published in Nature; Addresses and Lectures

Language is a third use which Nature subserves to man. Nature is the vehicle, and threefold degree.

1. Words are signs of natural facts.

2. Particular natural facts are symbols of particular spiritual facts.

3. Nature is the symbol of spirit.

When the eyes say one thing, and the tongue another, a practiced man relies on the language of the first.

The use of natural history is to give us aid in supernatural history: the use of the outer creation, to give us language for the beings and changes of the inward creation. Every word which is used to express a moral or intellectual fact, if traced to its root, is found to be borrowed from some material appearance. Right means straight ; wrong means twisted . Spirit primarily means wind ; transgression , the crossing of a line ; supercilious , the raising of the eyebrow . We say the heart to express emotion, the head to denote thought; and thought and emotion are words borrowed from sensible things, and now appropriated to spiritual nature. Most of the process by which this transformation is made, is hidden from us in the remote time when language was framed; but the same tendency may be daily observed in children. Children and savages use only nouns or names of things, which they convert into verbs, and apply to analogous mental acts.

2. But this origin of all words that convey a spiritual import, — so conspicuous a fact in the history of language, — is our least debt to nature.

It is not words only that are emblematic; it is things which are emblematic. Every natural fact is a symbol of some spiritual fact. Every appearance in nature corresponds to some state of the mind, and that state of the mind can only be described by presenting that natural appearance as its picture. An enraged man is a lion, a cunning man is a fox, a firm man is a rock, a learned man is a torch. A lamb is innocence; a snake is subtle spite; flowers express to us the delicate affections. Light and darkness are our familiar expression for knowledge and ignorance; and heat for love. Visible distance behind and before us, is respectively our image of memory and hope.

Who looks upon a river in a meditative hour, and is not reminded of the flux of all things? Throw a stone into the stream, and the circles that propagate themselves are the beautiful type of all influence. Man is conscious of a universal soul within or behind his individual life, wherein, as in a firmament, the natures of Justice, Truth, Love, Freedom, arise and shine. This universal soul, he calls Reason: it is not mine, or thine, or his, but we are its; we are its property and men. And the blue sky in which the private earth is buried, the sky with its eternal calm, and full of everlasting orbs, is the type of Reason. That which, intellectually considered, we call Reason, considered in relation to nature, we call Spirit. Spirit is the Creator. Spirit hath life in itself. And man in all ages and countries, embodies it in his language, as the FATHER.

It is easily seen that there is nothing lucky or capricious in these analogies, but that they are constant, and pervade nature. These are not the dreams of a few poets, here and there, but man is an analogist, and studies relations in all objects. He is placed in the centre of beings, and a ray of relation passes from every other being to him. And neither can man be understood without these objects, nor these objects without man. All the facts in natural history taken by themselves, have no value, but are barren, like a single sex. But marry it to human history, and it is full of life. Whole Floras, all Linnaeus' and Buffon's volumes, are dry catalogues of facts; but the most trivial of these facts, the habit of a plant, the organs, or work, or noise of an insect, applied to the illustration of a fact in intellectual philosophy, or, in any way associated to human nature, affects us in the most lively and agreeable manner. The seed of a plant, — to what affecting analogies in the nature of man, is that little fruit made use of, in all discourse, up to the voice of Paul, who calls the human corpse a seed, — "It is sown a natural body; it is raised a spiritual body." The motion of the earth round its axis, and round the sun, makes the day, and the year. These are certain amounts of brute light and heat. But is there no intent of an analogy between man's life and the seasons? And do the seasons gain no grandeur or pathos from that analogy? The instincts of the ant are very unimportant, considered as the ant's; but the moment a ray of relation is seen to extend from it to man, and the little drudge is seen to be a monitor, a little body with a mighty heart, then all its habits, even that said to be recently observed, that it never sleeps, become sublime.

Because of this radical correspondence between visible things and human thoughts, savages, who have only what is necessary, converse in figures. As we go back in history, language becomes more picturesque, until its infancy, when it is all poetry; or all spiritual facts are represented by natural symbols. The same symbols are found to make the original elements of all languages. It has moreover been observed, that the idioms of all languages approach each other in passages of the greatest eloquence and power. And as this is the first language, so is it the last. This immediate dependence of language upon nature, this conversion of an outward phenomenon into a type of somewhat in human life, never loses its power to affect us. It is this which gives that piquancy to the conversation of a strong-natured farmer or back-woodsman, which all men relish.

Thought is the blossom, language is the bud, action the fruit behind it

A man's power to connect his thought with its proper symbol, and so to utter it, depends on the simplicity of his character, that is, upon his love of truth, and his desire to communicate it without loss. The corruption of man is followed by the corruption of language. When simplicity of character and the sovereignty of ideas is broken up by the prevalence of secondary desires, the desire of riches, of pleasure, of power, and of praise, — and duplicity and falsehood take place of simplicity and truth, the power over nature as an interpreter of the will, is in a degree lost; new imagery ceases to be created, and old words are perverted to stand for things which are not; a paper currency is employed, when there is no bullion in the vaults. In due time, the fraud is manifest, and words lose all power to stimulate the understanding or the affections. Hundreds of writers may be found in every long-civilized nation, who for a short time believe, and make others believe, that they see and utter truths, who do not of themselves clothe one thought in its natural garment, but who feed unconsciously on the language created by the primary writers of the country, those, namely, who hold primarily on nature.

But wise men pierce this rotten diction and fasten words again to visible things; so that picturesque language is at once a commanding certificate that he who employs it, is a man in alliance with truth and God. The moment our discourse rises above the ground line of familiar facts, and is inflamed with passion or exalted by thought, it clothes itself in images. A man conversing in earnest, if he watch his intellectual processes, will find that a material image, more or less luminous, arises in his mind, cotemporaneous with every thought, which furnishes the vestment of the thought. Hence, good writing and brilliant discourse are perpetual allegories. This imagery is spontaneous. It is the blending of experience with the present action of the mind. It is proper creation. It is the working of the Original Cause through the instruments he has already made.

These facts may suggest the advantage which the country-life possesses for a powerful mind, over the artificial and curtailed life of cities. We know more from nature than we can at will communicate. Its light flows into the mind evermore, and we forget its presence. The poet, the orator, bred in the woods, whose senses have been nourished by their fair and appeasing changes, year after year, without design and without heed, — shall not lose their lesson altogether, in the roar of cities or the broil of politics. Long hereafter, amidst agitation and terror in national councils, — in the hour of revolution, — these solemn images shall reappear in their morning lustre, as fit symbols and words of the thoughts which the passing events shall awaken. At the call of a noble sentiment, again the woods wave, the pines murmur, the river rolls and shines, and the cattle low upon the mountains, as he saw and heard them in his infancy. And with these forms, the spells of persuasion, the keys of power are put into his hands.

3. We are thus assisted by natural objects in the expression of particular meanings.

But how great a language to convey such pepper-corn informations! Did it need such noble races of creatures, this profusion of forms, this host of orbs in heaven, to furnish man with the dictionary and grammar of his municipal speech? Whilst we use this grand cipher to expedite the affairs of our pot and kettle, we feel that we have not yet put it to its use, neither are able. We are like travellers using the cinders of a volcano to roast their eggs. Whilst we see that it always stands ready to clothe what we would say, we cannot avoid the question, whether the characters are not significant of themselves. Have mountains, and waves, and skies, no significance but what we consciously give them, when we employ them as emblems of our thoughts? The world is emblematic. Parts of speech are metaphors, because the whole of nature is a metaphor of the human mind. The laws of moral nature answer to those of matter as face to face in a glass. "The visible world and the relation of its parts, is the dial plate of the invisible." The axioms of physics translate the laws of ethics. Thus, "the whole is greater than its part;" "reaction is equal to action;" "the smallest weight may be made to lift the greatest, the difference of weight being compensated by time;" and many the like propositions, which have an ethical as well as physical sense. These propositions have a much more extensive and universal sense when applied to human life, than when confined to technical use.

Language is a city to the building of which every human being brought a stone.

In like manner, the memorable words of history, and the proverbs of nations, consist usually of a natural fact, selected as a picture or parable of a moral truth. Thus; A rolling stone gathers no moss; A bird in the hand is worth two in the bush; A cripple in the right way, will beat a racer in the wrong; Make hay while the sun shines; 'T is hard to carry a full cup even; Vinegar is the son of wine; The last ounce broke the camel's back; Long-lived trees make roots first; — and the like. In their primary sense these are trivial facts, but we repeat them for the value of their analogical import. What is true of proverbs, is true of all fables, parables, and allegories.

This relation between the mind and matter is not fancied by some poet, but stands in the will of God, and so is free to be known by all men. It appears to men, or it does not appear. When in fortunate hours we ponder this miracle, the wise man doubts, if, at all other times, he is not blind and deaf;

——— "Can these things be, And overcome us like a summer's cloud, Without our special wonder?"

for the universe becomes transparent, and the light of higher laws than its own, shines through it. It is the standing problem which has exercised the wonder and the study of every fine genius since the world began; from the era of the Egyptians and the Brahmins, to that of Pythagoras, of Plato, of Bacon, of Leibnitz, of Swedenborg . There sits the Sphinx at the road-side, and from age to age, as each prophet comes by, he tries his fortune at reading her riddle. There seems to be a necessity in spirit to manifest itself in material forms; and day and night, river and storm, beast and bird, acid and alkali, preexist in necessary Ideas in the mind of God, and are what they are by virtue of preceding affections, in the world of spirit. A Fact is the end or last issue of spirit. The visible creation is the terminus or the circumference of the invisible world. "Material objects," said a French philosopher, "are necessarily kinds of scoriae of the substantial thoughts of the Creator, which must always preserve an exact relation to their first origin; in other words, visible nature must have a spiritual and moral side."

This doctrine is abstruse, and though the images of "garment," "scoriae," "mirror," &c., may stimulate the fancy, we must summon the aid of subtler and more vital expositors to make it plain. "Every scripture is to be interpreted by the same spirit which gave it forth," — is the fundamental law of criticism. A life in harmony with nature, the love of truth and of virtue, will purge the eyes to understand her text. By degrees we may come to know the primitive sense of the permanent objects of nature, so that the world shall be to us an open book, and every form significant of its hidden life and final cause.

A new interest surprises us, whilst, under the view now suggested, we contemplate the fearful extent and multitude of objects; since "every object rightly seen, unlocks a new faculty of the soul." That which was unconscious truth, becomes, when interpreted and defined in an object, a part of the domain of knowledge, — a new weapon in the magazine of power.

Ralph Waldo Emerson Self Reliance

Ralph Waldo Emerson left the ministry to pursue a career in writing and public speaking. Emerson became one of America's best known and best-loved 19th-century figures. More About Emerson

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"Every man has his own courage, and is betrayed because he seeks in himself the courage of other persons." – Ralph Waldo Emerson

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The Nature and Origin of Language

The Nature and Origin of Language

The Nature and Origin of Language

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The most important is that it provides a good test for linguistic theories. It considers some current scenarios of the emergence of language. Some assume that language is a culturally evolved system of symbolic communication (Washburn; Dawkins; Byrne and Whiten; Donald; Deacon; Li and Hombert; Zuberbühler and Byrne; Dessalles; Kirby, Christiansen, and Chater). Others hypothesize that language is a genetically evolved system, as an adaptation by natural selection for communication (Jackendoff and Pinker), or as an adaptation in two steps (Bickerton), or due to a saltation for syntax (Hauser, Chomsky, and Fitch), or as an adaptation for a language of thought (Chomsky), or as an adaptation via grammaticalization of constructions (Hurford). However, they all fail to explain the emergence of the basic linguistic capacities—to create signs and combine them.

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The Nature of Human Language and Its Characteristics from a Semiotic Perspective

Profile image of Dr. Sarath W . Samaranayake

Human language is a remarkable and complex system of communication that distinguishes us from other species on the planet. It serves as a tool for expressing our thoughts, sharing information, and creating social bonds. The study of language and its nature has fascinated linguists, philosophers, and researchers for centuries, leading to various theoretical frameworks and perspectives. One such perspective is the semiotic view, which explores language as a semiotic system of signs and symbols. The semiotic approach to understanding language emphasizes the relationship between signs, meanings, and their interpretation. Developed by scholars such as Ferdinand de Saussure and Charles Peirce, semiotics provides a framework to analyze human language's structure, function, and characteristics. In this context, this paper aims to explore the nature of human language and its key characteristics from a semiotic perspective using a real-life scenario where we explain how a message is conveyed through signals and channels. Additionally, we examine the nature of human language, referring to the definition Bloch and Trager gave in 1942. Finally, we conclude by asserting that all human languages are equally important and necessary, with no one language being superior in structure, history, or biology.

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Studies in modern linguistic theory to determine the scope and vision of human communication have shifted their attention to semiotics, in which actions speak louder than words as some say. The semiotic capacity of an individual reflects the effective and efficient usage of pragmatic competence in which the language user has the awareness of sociocultural and anthropological conventions processed and produced in the course of communication. Such a capacity also enables a systematic usage of cognitive skills, thereby developing the value of the communicative context and the perception of the individuals in various discourses. This paper attempts to identify, decode, and proceed utterances in a systematic mixture of psychological, physiological, sociological and anthropological procedures, in which non-verbal expressions appear as signs and symbols to communicate information. It is also argued that not only do individuals attain semiotic information naturally, they also do so with pr...

Sarah Noori

The relation between semantics and semiotics is likely to be direct. Since semiotics is the science of signs while semantics is the study of the linguistic meaning of morphemes, words, phrases, and sentences .Through investigating the nature of words, for example, they are regarded as representatives of ideas and concepts. They are linguistic signs which are associated with non-linguistic entities via a bond. In an attempt to understand the essence of this relationship and the nature of bond, it is necessary to have an overview of semantics ,semiotics, the distribution of signs and the association between the two. Moreover, a brief view to the domain of semiotics and its impact on other methods of communication will be discussed.

SHORT COURSE of GENERAL SEMIOTICS

Abraham Solomonick

My first book on semiotics was published in Moscow in 1992; it was called "Language as a Sign System". After that, I wrote and published many books and articles, trying to understand the intricacies of semiotics and highlight its main characteristics. Over time, some of my views have undergone metamorphoses, and I have adjusted my earlier statements to express new formulations. Now, on the threshold of my 95th birthday, I want to sum up my vacillations and doubts in a short concluding essay, which seems to me worthy of attention. Whether this is really so, is for the readers to judge. I want to say a few words about what general semiotics means. De facto, semiotics originated in ancient Greece and Rome − no science or craft can exist without its own signs. But only at the end of the 19th century did a movement arise for the creation of semiotics that would formulate general principles for all branches of this science. In contrast to particular semiotics, such science can be called general semiotics.

Semiotica. Vol. 189. Pp. 271-285.

Frank Nuessel

Semiotics and communication are interrelated concepts. This review essay examines seventeen distinct perspectives on these concepts. They include the following: (1) detailed discussions of Charles Sanders Peirce's notion of the sign; (2) semiotics and semioethics in global communication; (3) critical and feminist approaches to signs and communication; (4) signs, communication and cultural systems; and (5) signs and artistic communication. The papers in this volume derive from a yearlong series of lectures sponsored by the Depart ment of Linguistic Practices and Text Analysis at the University of Bari (Italy) from November 1999 to May 2000.

Russian Journal of Communicaation

Alexander Kozintsev

Attempts at combining Uexküll’s ideas with those of Peirce within a single quasi-discipline called ‘biosemiotics’ are ill-founded. Peirce’s ‘interpretant’ sensu lato refers to two qualitatively different mental states, one relating to indexes and icons (INT 1) and the other to symbols (INT 2). Animal communication is dyadic – the referent is a directly induced mental state (INT 1). Glottocentric communication is triadic because the connection between symbol and INT 1 is mediated by INT 2. Whereas the gradualist view of glottogenesis is erroneous, Müller’s and Chomsky’s saltationist theories may imply that the idea of language Rubicon is antievolutionary. However, the views of Pavlov and Vygotsky and of their modern followers, Deacon and Tomasello, while being Darwinian, support the saltationist scenario. The emergence of the second signal system, of symbols, and of INT 2 was a psychological leap. In human communication, apart from the semiotic triangle (INT 1 – INT 2 – symbol), the dyadic relation between non-symbolic signs and INT 1 still holds.

Brent D. Ruben

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The Nature of Language

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Psychology Discussion

Essay on language and communication | human behaviour | psychology.

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In this essay we will discuss about language and communication.

Essay on Language:

Language is often described as the sine-qua-non or the most important and distinguishing characteristic of a culture or civilisation. There has been a the consistent relationship between the level of advancement of a society and the complexity and development of its language. In fact, one may say that civilisation or for that matter the very idea of knowledge is closely intertwined with language.

Scientists interested in the study of the evolution of behaviour of societies point out that there are four distinct features which have made the human organism distinctly superior to the highest evolved sub-human organisms like the chimpanzee. These are, attainment of an erect posture, the growth of the cerebral cortex and its complexity, the prolonged period of socialisation, and finally the acquisition of advanced and complex linguistic capacities and abilities.

While the first three are purely biological or related to biological factors this is not the case with language. Language skills of the human being have been growing and will continue to grow. In fact, while the brain structure, erect posture and socialisation have essentially remained the same throughout human history, the story is different in the case of language.

Languages have grown in complexity, quality, flexibility, finesse and versatility. While animals and to some extent even the earliest human beings mostly depended on gestures and movements, in contemporary society, verbal language is the most characteristic medium of interaction.

This verbal language has come to engulf our lives in every sphere and today one cannot think of advances or development in any sphere of life without the involvement of language. Thus, human society has evolved music as a language, the numerical system as a language, dance as a language, Morse code and today a variety of computer languages.

Nature of Language:

Language is essentially a set of signs and symbols which have certain fixed meanings, evolved in each society. It is this fixed denotation which makes languages so essential and crucial.

The signs or symbols may be sounds, words, light signals, gestures, facial expressions, geometrical signs, and body postures. These signs can be verbal or non-verbal, visual or auditory, animate or inanimate. Thus a traffic light is an inanimate visual sign.

The telephone bell is an inanimate and auditory symbol while the expression of a dancer is a visual and animate symbol. When you get up to interrupt your teacher while he is teaching, he makes a gesture and you sit down. This is visual and animate. Thus, we see that when we talk of a language, it is just not the mere verbal language that we mean though that represents the most complex and advanced from of language behaviour.

It is obvious that any language can develop only in a society or social context. So, the nature of the society and social interactions play a very crucial role in determining several aspects of language, particularly the linguistic form of language. This is true both at a collective level, the evolution of a language, and also at the level of an individual, the acquisition of language abilities.

Of course, there is a view that certain forms of language structure are universal and innate and that even some animals exhibit some sort of language behaviour. But, even if there are innate and universal language structures, it is undeniable that verbal language is very much a product of social life interaction and social evolution.

It is estimated that human beings have used some form or the other of a spoken language for more than a million years or even three million years. However, written language is estimated to be only about 7,000 years old. This latter perhaps is an underestimation, and even written language is probably older than this.

This clearly shows that language behaviour in a spoken or written form has been very closely associated with the whole evolution of human societies. Languages appears to have played a very crucial role in social evolution, the emergence of civilisation and unfolding of social life in all its aspects including social degeneration.

If an individual who lived in this world a million years ago and who was speaking some language of his period were to come alive again, he will be amazed at the complexity of modern languages. Perhaps, he will not be able to recognise them as languages. Thus, modern languages have become much more complex, complicated, rich in vocabulary and above all show variance from one language to another, some with a long vocabulary. Idioms and sentences are combinations and re-combinations of a limited number of basic sound units called phonemics corresponding to vowels and sentences though not exactly the same.

The number of basic phonemics used by different languages in the world are limited and range between 15 to 85, the English language using about 45 such phonemics. Combination of these phonemics in different ways and manners leads to the formation of morphemes which some linguists compare to words though morphemes are not exactly similar to words and certainly not identical.

It is sometimes estimated that a very highly educated individual learns to recognise about 1, 00,000 morphemes out of the basic 45 phonemics the average being around 10,000. A few studies by Templin and Irwin have shown that there are very wide individual variations depending on socio-economic status, educational background, intelligence, availability of learning opportunities, etc.

A number of other factors also seem to contribute to these variations. Thus, we may see how fantastic languages are in that, though their basic units are limited, their combinations in various ways have resulted in the generation of unlimited number of morphemes. We may thus say that language is an open system, even mild variations in the intonations of these phonemics can result in new morphemes.

Evolution of Language:

One observation which has been engaging the attention of life scientists including psychologists is that many of the activities which we see at the human level are also seen in the behaviour of sub-human organisms. This interest is a direct result of the Darwinian theory of evolution.

Stated simply the question runs as follows:

Do sub-human organisms exhibit and employ a particular activity or activities seen at the human level, and if so how far down the levels of the evolution ladder this activity is seen?

Further, if these activities are found to be present at the lower levels do they serve the same purposes as they do at the human level? Do they show the same degree of complexity and individual variations? Are the differences between sub-human forms of such activities and human forms different only in degree or are they also qualitatively different?

This search for the presence of human level activities at the level of the lower organisms has been extended to languages also. Scholars have been trying to explore whether there exists some forms of social interaction among animals based on languages. Some of the earliest and classical studies of Wheeler and Fabre have shown evidence for the existence of some elementary forms of social organisation among animals and insects.

It has also been shown that some forms of social interaction and communication exist among birds. A number of studies have been undertaken on sub-human organisms at higher levels like apes and chimpanzees who are the immediate evolutionary ancestors of the human beings.

Such studies have shown that they live in clans and also possess embryonic forms of power hierarchy. This being so, it is only logical for forms of communication to exist among these animals, may be these forms of communication are not linguistic.

If we define language as essentially a system of signs used for expressing and communicating, then there certainly appears to be some form of language though not necessarily phonetic in form. A number of studies involving observation of animals in situations involving coping with certain crises situations have shown that at the levels of animals while one may not find linguistic and verbal communication, nevertheless a variety of body movements, sounds and even emission of certain odours exist and play a definite communicative role and perform functions including expression, influencing, signalling, etc.

Thus, certain sounds are made when an enemy is seen as an expression of a felt threat which in turn acts as a communication to other members of the group, thereby influencing their action and making them run away. Such sounds or actions constitute ‘display or exhibitionistic language’ to communicate with other members.

Among the geese, a set of vocal displays have been repeatedly observed to prepare to escape as a group. Vocalization or other forms of sounds have been found in birds during hatching seasons and these vocalizations set in motion a general reaction to make other birds react in a similar manner. Touch language is also prevalent as preliminary to mating between two members of the same species – one male and the other a female.

Animals at the higher levels of the animal world like apes and chimpanzees have the ability to acquire, store and retrieve information to a much simpler extent than in the case of the human. If this is the case then, at-least at the level of the chimpanzee, there should be some elementary form of language.

A strong case for the presence of such a basic language at the level of the apes has been made out by investigators like C.R. Carpenter. Carpenter and many others have identified anywhere between 9 to 20 sounds with different meanings used by apes. But the one observation has been that while such a sound language may initiate action in other members of the species, nevertheless there is no dialogue or conversation.

The language and communication seems to operate one way. Further, they are found to be used only in emotion led situations and are very situation specific as in attracting a mate or signalling the arrival of an enemy. Thus, situationised considerations of direction and guiding physiological action seem to be the main functions of such sounds. Of course, there is nothing purely verbal and in that sense nonverbal language, speech or communication among members of lower organisms seems to exist.

One may then conclude, if we look at language purely as a tool of expression and communication directed to influence the behaviour of others, there does seem to be adequate evidence to indicate the existence of some rudimentary forms of language among animals and this is in parts in the form of physical movements or sounds or directly involve display, situation specific and one way. Animals do not know how to use a word or a sentence or for that matter any form of verbiage.

Certain investigators however, have gone a little further and tried to find out whether even if there is evidence in the case of higher level animals like gorillas or chimpanzees about their having a language, do they have the structural capacity of the brain? This has been doubted by another group of investigators.

Similarly, one can raise the question, can we teach human language to the apes? A few interesting studies have been undertaken in this regard. In a very interesting study along these lines, Keith Hays and Cathy Hays adopted an infant chimpanzee Vicki and brought her up as a human child.

Despite intensive efforts they found that all attempts to teach Vicki to speak like a human child were futile, Vicki being able to utter hardly three barely recognisable words or sounds after three years of teaching. But at the same time, they found that although Vicki could not utter these words, she was able to comprehend many more words of the English language. Thus, in many other chimpanzees who were studied, it was found that though there is no ability on the part of the animals to utter or speak, they were able to understand and interpret.

In view of this, it was concluded that human language is unique to the human species because they are distinct from animals. But very soon the Pandora’s box was opened again. Reacting to the earlier findings that even man’s closest animal cousin, the chimpanzee could not learn the languages, critics started asking that while the chimpanzee may not be able to speak, speech is not the only component of language and that there should be other aspects of language like comprehension, interpretation, etc., and it is possible that as in the case of speech, if these processes were also investigated then the chimpanzee may be found to be more capable.

Human infants are not capable of producing many speech sounds. It has been found that this is because of their underdeveloped vocal tracts and memory and also because of their inability to make certain articulate movements needed for speech production. Lie Beevan and others found that many mature non-human primates faced the same problem as human infants.

Their vocal tracts are smaller like those of human infants. On the other hand, studies intending to find out whether primates perceive the speech sounds, in ways similar to the ways perceived by human beings, have produced mostly negative results or at any rate results which are not clear.

Overall, there appears to be a broad consensus, perhaps, tentative that primates including chimpanzees are not well endowed with the capacity to acquire the ability to communicate using speech. But, what about other aspects like comprehension?

Children who are not able to speak certainly appear to be quick in other aspects like responding, interpreting, comprehending, etc. as observed by Lenneberg and Maclean and Ruches. Beatrice and Garner undertook a pioneering investigation attempting to teach American sign language to Owashowe – a chimpanzee. Owashowe was brought up in the house trailer, interacting with a number of human beings who while in her presence never spoke verbal languages but used sign communication extensively.

Signs were used to communicate objects; questions were asked in sign form. In the beginning the progress was very slow. But by the end of about 22 weeks Owashowe could acquire a vocabulary of nearly 34 signs and use these under appropriate situations.

Though like human infants, Owashowe’s sign language initially was not very descriptive and clear, gradually situation appropriate and specific sign language was achieved. More remarkably, Owashowe after a number of minutes could combine such signs to produce sentences like “you drink”, “key open” etc.

According to many investigators like Brown, Owashowe’s language was very similar to what human children in the first stage of speech have. Other investigators like Premark based on their studies on chimpanzees arrived at more interesting conclusions.

The task involved here was slightly different. Here, the chimpanzee Sara had to learn to equate certain utterances with a set of distinctly coloured and shaped plastic pieces arranged in a pattern and stuck to a plastic board. The utterances were a pattern of sticking together a set of such pieces. Results were really surprising and the chimpanzees could learn this language to a very high degree of complexity.

Here, the attempt by the experimenter does not require the animal to translate the language into human language terms. She only had to learn a non-verbal way of uttering what was uttered by the investigator. Researches like this show that while chimpanzees may not go far in learning a human language and speak the same, their ability to code, encode and decode symbolic cognitive inputs like colour pieces, cannot be written off.

Research in this area has certainly pointed out that even at the higher level of evolution, the animals are not very capable of learning and speaking the human type language and produce speech sounds. But, because of this, one cannot say with the same degree of certainty that they do not comprehend language symbols, or forms of relations among them.

The adult human beings’ capacity for mastery of human speech is infinitely superior to that of an adult chimpanzee, but at the same time there is a lot of similarity between the vocabulary of a human infant and a chimpanzee. A chimpanzee can learn a language of different types.

Of course, it does not mean much, but one or two sounds become clear. The nature and structure of the brain and the vocal apparatus at the human level thus make a qualitative and quantitative difference in the language behaviour of sub-human organism and the human being.

Apart from this basic biological structure, there are greater number of language based interactions, and also more numerous opportunities for mastering a language, at the disposal of the human being which definitely make for a difference between human language and sub-human language.

Functions of Language:

If language is such an important part of human life, then it certainly must have served human beings very well and also useful purposes. What are the various functions of language? Essentially, as a common man sees, language has two obvious functions; first it serves a person to express oneself and then communicate.

Through an expression one is able to inform, request, persuade, threaten or influence others. This means one is able to relate one’s experiences, fears, wishes, to others and similar experiences of others. This is the beginning of social life. When people are able to express to each other, naturally they are able to interact with each other and understand what others are saying or doing. Based on such an interpretation there is the beginning of action.

This phenomenon goes on. Thus, we may see that language helps in expressing, understanding, interpreting and communicating events those inside a person and when there is a reciprocal action one can see how social interaction starts. The entire fabric of social life is thus based on an ability to express, interpret, understand and influence, first reciprocally, then in an extended manner.

Thus, one may see that language is at the very foundation of human civilisation. Perhaps, this is a very simple statement of what is happening. When I say I understand and interpret, there is a cognitive function; when I say, make others accept or act, there is a social function, one of relating myself to others.

When I talk and smile, this is an affective function. Language helps people to develop, shape perception, share interpretations, share expectations, and share expressions and also feelings and emotions. We may now briefly examine some of the basic functions of language. Some of these are obvious and others are not so obvious.

Let us for a moment imagine that a sub-human organism or a primitive human being faces for the first time a condition of hunger or fear, sees another strange animal or hears a sudden noise. Cutting across all these one would have faced a condition of disturbed equilibrium which might have resulted in the feeling of fear, surprise, joy, delight, hunger, or whatever it is.

However scientific our modern researches may be, common sense tells us that the earliest form of linguistic reaction must have originated under such a situation. A classical example is the birth cry of a new born child.

In all these instances, it may be seen that the origins of language lay in an articulation or expression of the state of the organism; but while being an act of expression, it also becomes an act of trying to understand and interpret the strange feelings or state of affairs.

Now, we may begin our attempts to understand various usages, and functions served by language or linguistic behaviour which includes the sound language of some animals and also the sign language used in various categories.

The functions of language can be classified under two or three broad categories – 1. Expressive and Communicative Functions 2. Interpretative Functions 3. Control function 4. The Functions of Remembering and Thinking 5. The Discovery of One’s Name 6. Social Functions of Language 7. Creative Functions.  

Specificities of Languages:

Various languages spoken by different groups of people differ in as many dimensions as there are to language behaviour. The simple language of a stone-age tribal community is far different from one of the developed and complex languages of today. Not only this, languages also change, grow and evolve.

For example, some languages like Latin or Sanskrit are referred to as dead languages in that they have not changed over a time because of not being actively used, particularly in speech, and also because they have not interacted with other languages. But, amidst all these variations across space, and over time, are there any universal characteristics and structures of language?

Here again there is a debate with one group of scholars who argue for the existence of universal characteristics, others deny the existence of any such universal characteristics. But the controversy notwithstanding, there are some universal characteristics of language.

Some of these are as follows:

a. Discreteness :

The message (words and sentences), in any language are brought out from a limited number of units. For example even if you utter a word like ‘brother’ in different ways the listener will understand the word in the same way. Thus in-spite of differences between American spelling and British spelling of the word ‘colour’, it means the same to all those who know English.

b. Arbitrariness:

Language terms are arbitrary. No one can explain why an elephant should be called an elephant and a man by the word man. There is no reason, or if there is any reason, we do not know. Of course, there are some words in every language where one can see a similarity between a word and the object, it denotes.

For example the word ‘kaka’ in Tamil means a crow. This word is based on the sound of the crow crowing. Such a connection is called onomatopoeic – similarity in sound. This means that tomorrow if we decide to call a cat as a cow and a cow as a cat, there is nothing to stop it.

c. Openness :

As pointed out, in every language new terms, words and messages are generated easily. Every language grows, and the number of words, sentences and idioms keep on growing depending on experience, increasing complexity of life and interaction with other people and other languages. This means no one can claim that he or she has completely learnt or mastered a certain language or the messages in a particular language.

These three characteristics-discreteness, arbitrariness and openness are universal features of all languages. The presence of such universal features has raised the question as to whether there is a certain universal language or linguistic structures present in all human beings cutting across languages and therefore, not language specific.

A leading advocate of such a view is Chomsky. According to Chomsky there are some universal structures or formal operations in languages, which underline the semantic or meaning aspect. These theorists have been trying to identify certain universalities, similarities and regularities in language behaviour across language and cultural variations.

It is hoped that such research can ultimately help in building up a universal grammar. Once such universal grammar is developed, then it is easier for one person to learn another language. But more than this, if this possibility becomes true it will help us to achieve a better understanding of the entire system of cognitive processes including speech, memory, learning, thinking and perception.

Such a view would help us to understand the innate biological processes and necessities which condition language behaviour. According to Chomsky, language behaviour is not purely learnt by accident or conditioning and much of it is biological and species-specific.

Bio-Neurological Bases of Language:

The human being is basically a biological organism, born as a biological creature becoming a social and psychological organism. Certainly, some views hold that the human being is inherently social.

Assuming that the adult human being is more social than merely physiological, it may be pointed out that, elementary forms of social behaviour are evident even in lower animals. While the human being may be much more social and complex, social nature of behaviour is not an exclusive privilege of the human organism.

Secondly, all social actions of the human organism take place only through the available bio-physiological mechanisms and if human social behaviour is much more advanced than that of the lower organisms, this is very much because of the highly advanced and developed body system he or she is endowed with, particularly the human brain.

All human actions therefore, have their basis in physiological and neurological possibilities. This is true of language behaviour including speech behaviour. The question is, how far is language behaviour including speech is determined by biological endowments. Here theories of language behaviour differ, in the degree of importance they attain biological mechanism.

Though no theory questions the essential minimal requirements of the biological equipment and mechanisms for achieving normal and effective language behaviour, some scientists like Chomsky argue that there are innately endowed biological language structures which are universal. Lenneberg believes that the unique human pattern of communication is possible only because of certain biological propensities and possibilities for complex language behaviour, particularly speech.

According to Lenneberg, there must be clear specialisation in the brain in relation to its anatomical structure and other speech related mechanisms. Further, the fact that children across the culture and sub-culture show a lot of similarities in language and speech behaviour indicates that there should be a regular and uniform pattern of development in children regardless of socio-cultural variations. Lenneberg further states that there ought to be innate and biological processes of the system which makes language development possible in spite of many handicaps and disabilities.

The failure of sub-human organisms to acquire comparable language and speech abilities, according to Lenneberg is a further proof of the unique and distinct structure and specialised characteristics of the human body particularly the cerebral cortex. Finally, Lenneberg cites the existence of language universality in phonology, syntax, grammar, etc. as evidence for the existence of universal and strong biological bases.

Essay on Communication:

One of the basic functions of language is communication. Communication plays a very important role in our lives. We communicate with members of our family who are living with us, with our friends, with our colleagues, with our bosses and everyone including a pet-dog. Let us not forget that we communicate with ourselves.

Of course, this is not the same as talking to oneself. We communicate with people who are present with us. Thus, when your mother, or the father or the teacher says something to you, this is called direct communication.

Similarly, when you talk to your friend on the telephone, this is also called direct communication. But if you are leaving on some urgent work and ask your brother to pass on a message to your parents or some other friends, this is indirect communication.

Here you are passing on a message to one person through some other person. This communication is not direct, but indirect. Similarly, a teacher teaching to a class of pupils is engaged in direct communication. This is communication between an individual on one side and a group of people on the other.

So is the case where a chief executive officer of a company calls for a meeting of his senior colleagues and addresses them; this is again direct communication with a group. On the other hand when the same chief, instructs these senior executives to pass on a message to other officers of various branches, this is an individual communicating with a group, but indirect.

Thus, in direct communication we communicate with those for whom a message is meant and in indirect communication we communicate with those to whom there is a message through somebody else and the concerned people do not receive the message themselves from us.

Now what is communication? Essentially communication is a form of social interaction where two or more people are involved. There is a transmission and exchange of information, knowledge or message. When you go to a railway booking office and find out whether accommodation is available by a certain train, you get the answer as to whether it is there or not.

Here you are seeking some information and you get the same. But in a classroom, the teacher passes on not only information but also knowledge. Newspapers provide information. But, if you are reading a book on a particular subject, you get knowledge. On the other hand if you are writing a letter to a friend or talking to him on the telephone informing him that you will be reaching him the next day at a particular place and time, this is a message.

Generally, the term communication is used to describe the kind of interaction between two or more individuals where one person or a set of people interacts with others with the intention of influencing the opinions or actions, of the latter. Thus, an advertisement is a piece of communication where the advertiser wants to influence people to buy a particular product. Clearly there is an intention behind a communication.

Along with the intention, there is also an expectation as to whether the other person or persons would do what you want them to do. Thus, when you leave a message for your friend that you would be meeting him at a particular place and time, you have the intention of asking him to wait for you and also expect him to wait for you or call you back to tell you whether it is possible or not. The degree of expectation varies.

The advertiser, for example, cannot be certain that everybody who reads his advertisement will buy his product. But when a boss sends a message to a subordinate asking him to wait for him, his expectation is more. Thus the degree of certainty is decided by intention and expectation. And even if the intentions are strong, and the expectation is low, the communication may not take place. On the other hand, if both are strong, communication will take place.

Thus, whether communication occurs or not is decided by the strength of intention and the certainty of expectation. We may say that communication arises whenever there is an intention or need. Of course, factors like availability of means also decide whether communication will take place or not.

Communication involves symbols and signs. Thus, every communication involves words, gestures, movements, etc. At the human level, communication is to a large extent verbal or involves words, numbers, symbols, etc.

This type of communication involving language or related symbols is known as verbal communication. But a large part of our communication also uses non-verbal symbols like gestures, movements, lights, sounds, etc.

The traffic signal is a clear example of non-verbal communication using light symbols. The horn of an automobile behind you is an example of a non-verbal communication with a sound symbol where the driver behind intends to overtake and expects you to give him the side clearance.

Gestures are also commonly employed as in the case of the traffic constable who gestures with his hand to the vehicles coming from a particular direction to stop or move. Similarly, you are sitting in a class and your friend standing outside is asking you to come out with a gesture and you ask the friend to wait for sometime with a gesture. The umpire on the cricket field raises his finger to communicate to the batsman i.e. out, and expects him to leave. Touch is also a means of communication.

If you are sleeping in the classroom and the teacher is about to notice the same, your friend touches you and you get up. You touch or fondle a little child or a pet to show your affection. In lower organisms, even smell is used as a communication. Thus we see that the communication can make use of any sensory modality, visual, auditory, touch and smell and can involve words, sounds, figures, lights, signals, gestures, etc.

Non-Verbal Communication and Body Language:

Though verbal language is our major medium of communication, there are other forms of communication and also that, any speech is not a piece of communication. Further, in many situations we speak not only with our mouths, or words but also through our body movements, expressions of the eye, posture, etc. A speaker uses a lot of gestures, modulations of voice, movements like bending, pacing up and down and does many other things to make the communication more effective.

In recent years, there have been a lot of research studies trying to understand the role of non-verbal communication including body movement, expressions, etc. on the effectiveness of communication. Such movements, expressions, gestures, etc. have all come to be known together as ‘body language’.

The study of the role of gestures, and body movements in the process of communication has resulted in the emergence of a specialised field of study called ‘kinesis’. Attempts have been made to prepare a dictionary giving a list of body movements and the meaning they generally convey.

An American anthropologist, B.T. Hall based on a very careful study of postures, degree of bending, angle of vision, etc. employed by people of different cultures, has argued for a discipline of study called proxemics which is interested in the study of how people use timing, body posture, and distance to make the communication effective.

Body language is widely employed by lower organisms and it is also used more extensively in simple human societies where verbal language has not developed to a very high degree. People employ body language very often not as a part of conscious effort. This just flows as a supplement to reinforce and strengthen the verbal communication. However, today body communication experts are attempting to train people to use body language selectively and more effectively. In fact, dance is a learnt and organised form of body language.

Non-verbal or body language communication has been evident in arts like dance, sculpture, music, etc. from ancient times. But the disciplines which study its status as a means of communication today are linguistics, anthropology, history, clinical psychology, etc.

Some of the scientists of these disciplines brought together a long list of expressive movements in the form of a dictionary, thus, trying to associate specific meanings, motives, etc., which underline them. Some of these movements are blinking, fingering the nose, crossing the finger, finger or knuckle cracking, loosening the collar, shrugging the shoulders, shaking a leg or legs, etc. However, this type of research has a long way to go.

Psychoanalytic literature, beginning with the writings of Freud, contains many explanations of the relationship between expressive movement or gesture and an unconscious motive. For instance, according to them, blinking the eyelids may indicate a desire to conceal something or the desire to hide from others. Dittmann studied pattern of movements composed of interaction between head, hands and legs for five different moods.

The frequency of movement within each of the body segments was arrived at from motion pictures of a patient during psychotherapy. The moods were judged in accordance with what the patient was expressing verbally. It was found that anger correlated with increased movements of the head and legs, with the hands remaining inactive while a depressed mood correlated with increased leg motion, both head and hands being inactive.

Wilhelm Reich, a leading psychoanalyst, after years of working as a therapist, began to notice that people’s facial expressions, gestures, posture- their body language -often told him more about their feelings than their words. Shaking a leg while talking about one’s wife’s temper tantrums, a drooping mouth when talking about a dead child, blinking frequently and closing eyes for a longer duration, holding the lips tight when talking about sex, etc. – all these movements were extremely revealing.

Pursuing this observation, Reich began to see muscle tension as the bodily equivalent of psychological blocks and defences. Tension protects a person from threats and the dangers he does not think about consciously. People hold their breath, stiffen their arms, tense their necks and shoulder muscles when they are on the defensive.

Rigidity not only protects the person from external threats but also prevents the free flow of emotions. Reich began looking for a way to relieve these tensions. Ida Rolf, who was trained as a bio-chemist, arrived at much the same conclusion through her work in physiology.

When a person is injured, the muscles in the area tighten to compensate for the injury. Often compensation becomes habitual and persists long after the injury has healed, i.e. the tightened muscles lose flexibility. Perhaps the body reacts to emotional traumas in the same way it reacts to physical traumas. Ida Rolf began to look for a way of restoring ‘structural integration’.

Both Reich and Rolf found, to their surprise, that when they treated knotted muscles with massage, clients invariably became intensely emotional. Fears, traumas, old anger and old pain stays ‘locked in their muscles’. For many people, physical therapy seemed to promote much deeper emotional release than verbal expression which is the essence of psychoanalytic therapy.

Interest in this approach seems to be growing day by day, but it needs to go a very long way before it can shake, let alone topple, the concept of ‘verbal expression’ as an ‘exclusive emotional releaser’.

Effectiveness of Communication:

Communication is normally initiated by some individual or a group with the aim or intention of influencing the behaviour of somebody else by sending a message through a channel or a medium. It was further noted that communication plays a very crucial role in our lives.

Of course, there are instances where people get influenced by communication not specifically directed towards them, but overheard by them. Similarly, there are also instances where a communication intended to influence some particular person or persons, influences people who are not intended to be influenced. By and large every communication has an intention of influencing the ideas and behaviour of some specific set of people.

This intention can be achieved only if the process of communication takes care of certain requirements. A communication which is able to influence people in the intended manner is said to be effective. Of course, communications vary in degree of effectiveness. Some communications are more effective than others that too for sometime and not always.

The effectiveness of a communication varies depending on a number of factors. These factors have been studied extensively and certain findings have emerged which have enabled people, particularly in organisations to make communication more effective.

In every communication there is a source, a person or persons who initiate the communication, a channel like a letter or a telephonic message or signal, etc., a message which is the essence which conveys to the other person what is to be done and a receiver or audience, a person or group of persons to whom the message is directed and who are intended to be influenced. Source, channel, message and the receiver are the four important components of a communication process and all of them are important in deciding how far a particular communication is effective.

We may briefly examine how these four components can be carefully planned and built into a communication process so that the communication can achieve what it intends, to a large degree. A company may become more effective in making the people buy its products, and a political leader may influence people to vote for his party; all these can be achieved better by designing the process of communication in an effective manner.

1. Source Characteristics:

It has been found that a number of characteristics of the source contribute to the effectiveness of the communication. One such factor is credibility. Credibility refers to the perceived importance of the person. Thus, when the Prime Minister or any other highly placed person makes an appeal, people respond.

This is because the person is accepted as genuine and sincere and also capable of carrying out what he says he can do. Thus, when an expert on a subject gives some new information we accept it. When a highly qualified doctor prescribes a treatment, the patient accepts it.

We accept the authority, legitimacy, sincerity and competence of the person. In an experiment students were shown a passage of poetry and asked them to rate the same. Two groups were involved. One group was told that the poem was written by some unknown person and the other group was told that it was written by a great poet.

The second group rated the poem as of a much higher quality. But a question has been raised as to whether this credibility is very specific or general. For example, if we attribute an article on economics to a leading poet, will this have an effect on the rating of the article? On this question, research studies have brought out contradictory findings.

Some studies show that the credibility factor is specific and that an article on economics will not be rated higher if it is attributed to a poet or a film star whose credibility may be high in influencing us to buy a hair cream.

However, there are studies, which argue that there is a general effect of credibility and people will accept a film star’s advice, even on whether India should manufacture nuclear weapons, or whether the constitution should be amended to declare film stars as super citizens.

Closely related to credibility is trustworthiness. A person may be an expert. But if earlier communications from the person were found to be unrealistic and misleading, then there may be a lack of trustworthiness and this may counteract against the factor of credibility.

Thus, if a person who is perceived as corrupt tries to influence the moral behaviour of people or asks them to contribute to a welfare programme, the effect may not be much, even though he may be perceived as competent or having a position. Other things being equal, if a source is perceived as a person who can reward or punish, this may have an effect on the effectiveness of the communication.

Yet, another factor appears to be the factor of similarity between the person who sends the communication, the source, and the receiver. Thus young people are generally more influenced by sources which are similar in educational background, age, background and status. Thus, it has been shown that characteristics relating to the sources do have a crucial influence on the effectiveness of the communication.

2. Channel Characteristics :

Channel is the medium by which the message is passed and presented. The channel may be direct and personal or through media like telephone, radio, newspaper, etc. The choice of the channel or medium depends on a number of factors like the nature of message, its coverage, importance, whether it is private, the size of the audience, their characteristics, etc.

Research studies have shown that direct communication is more effective especially as it permits use of body language like the expressions of the eyes, posture, etc. In terms of distance, a distance of about four meters between the source and the receiver has been found to be effective. Here again a number of factors have to be taken into account. For example, in a classroom a teacher cannot maintain a distance of four meters from all students. Nor is it possible in any direct audience situation.

One issue which has been investigated in more detail and depth relates to the question whether films or audio-visual aids are always more effective. Some studies have shown that films are more effective in communicating factual information and sometimes also in bringing about attitudinal changes, but there have been other studies which have shown that if the receiver is more educated and mature, printed communication is more effective.

The fact appears to be that effectiveness of a channel or medium seems to depend upon a number of factors like the nature and size of the audience, the nature of the message, the time available, the urgency of the message, etc.

3. Message:

The message is the core component of any communication. If there is no message, there is no communication, even though people may be talking. Some studies have attempted to study some of the necessary characteristics that may contribute to more effective communication. One important characteristic is known as loading which refers to the amount of information in that communication.

For example, if a boss wants to ask one of his own subordinates to go and meet and discuss a particular issue with somebody, he may simply say “regarding the matter, please meet Mr. A. at 3.30 p.m. today and talk to him”.

This is simple, brief and direct but adequate. If he says “I was with Mr. A yesterday and we had dinner. While talking to him I found that he has lot of experience in matters related to the issue which we discussed two days ago. You try to meet him and see if he can help us.”

Here we can see that the amount of unnecessary information is much more than what is necessary for the young person to act and it is possible that the message is too elaborate and confusing and the receiver misses the essential part. Such a lengthy communication with too much of unnecessary details is said to be overloaded.

On the other hand the boss may tell the young person, “meet Mr. A, and I want to have a discussion with him”. This is certainly brief but not very clear to the person who is expected to discuss with Mr. A. He is not sure about what to discuss with A, where and when. Such communications will create a need for a series of further communications on various aspects.

This is an example of what is called communication under-loading. The message should be optimally loaded while clearly communicating whatever information the receiver needs to carry out the instruction contained in the message. Thus, ‘loading’ is found to be a very important characteristic.

Messages may be of different types. Some of them may be a ‘one-way matter’. The source may expect the receiver to do something only once, but there could also be a message where a choice of action or response becomes necessary among different alternatives. This is very true of communication relating to work organizations.

It is very common that a particular person is involved in a number of transactions which are interrelated. The boss may find it necessary to send a message to him to take a particular action on a particular issue or matter. It is also possible that sometimes there are conditional messages.

For example, the boss may instruct the receiver as follows “meet Mr. A and find out what is happening. If there is some problem, ask him to talk to me and if he is not there meet Mr. R”. In such instances, it becomes difficult or even impossible for the respondent to get the message clearly unless the message is con-texted properly by clearly giving the necessary background and details.

Very often an organisation may be involved in a number of transactions involving the same client or party. So unless the background relating to a particular transaction on which action is to be taken is made clear, there may be a delay or even a wrong action at the end of the receiver.

This involves not only clarity of what to do, but also on what matter the action is to be taken. The message must provide the necessary details for the receiver to clearly identify the concerned issue. We may call this factor as “contesting or embedding”. Yet another characteristic refers to explicitness or implicitness or we may even call them degrees of explicitness.

Suppose a client is filing a legal case and his lawyer after studying all the details, comes to the conclusion that there is very little chance for the client to win the case. He may convey this directly to him and straightaway advise him to withdraw his case and arrive at a compromise. This will be an example of explicit communication.

On the other hand, the lawyer may explain to him all the details and also instances of similar cases he has handled in the past and leave the client to arrive at his own decision. This is an instance of implicit communication. Here again in-spite of a number of studies, no definite conclusion appears to be available.

Some studies by Hovland and Mandel on influencing American public opinion on the need for devaluing the dollar found that presenting an explicit conclusion was found to be more effective, but equally strong is the evidence in support of the strategy, where the message is presented without an explicit conclusion leaving it to the receiver to arrive at the conclusion. A classic example of this latter type is the oration of Mark Anthony on the death of Caesar where without explicitly inciting the people to revolt, he succeeded in making them do it.

Another message characteristic that has been investigated is with regard to the couching of the message in emotional appeals, emotional overtones and invoking reactions like love, loyalty, patriotism as part of the message. There are a number of studies on employing fear as an overtone. Extreme fear appears to have been occasionally found to be effective, but not always.

According to Janis effectiveness of fear appears to be associated with a number of factors. Appeals for dental care and hygiene were found to influence attitudes and behaviour relating to dental hygiene in inverse proportion to the degree of fear. This was shown by Janis and Fish back who found that the more intense the fear appeals were the less was the effectiveness.

One the other hand, association of moderate fear appears to be more effective. On the other hand studies by Levianthal and Nice and Singer on appeals in connection with traffic safety rules and traffic signal observance, showed that intensity of fear appeals had a greater effect.

By and large it appears fair to conclude that on the whole moderate fear appeals have a greater effect on more people, than extremely high fear or extremely low fear appeals Yet another finding was that messages for change of attitudes along with fear appeals were more effective when the message in the communication suggested ways to overcome the fearful situation.

Other aspects of the message including length, dramatization, medium, etc. have also been studied from the point of view of effectiveness in .bringing about changes in attitudes. The factors of primacy and recency, whether a communication received earlier or more recently is more effective has also been researched upon and the findings are far from conclusive let alone unanimous.

The contribution of each of the factors seems to depend on many other factors like nature of the content, demographic background of the audience, the perceived importance and even personality factors of the receiver. However, these studies have certainly exploded some myths like the universal effectiveness of emotional appeals, primacy, etc.

4. Receiver:

The receiver is the ultimate user of any type of communication. Communications are generally directed towards influencing the receiver, his opinions, attitudes, behaviour, etc. All receivers are not similar. There are group differences and individual differences. The susceptibility of the receiver to the influence of communications is called persuasibility which indicates the proneness of an individual to change in response to a communication.

Sensing and Brehm based on a series of studies have argued that after a certain stage, there emerges a condition which may be called ‘reactance’ marked by resistance to succumb to persuasion. This reactance can vary from simple indifference to positive hostility. This is a very important point for those who believe that mere volume and intensity of persuasive appeals can persuade anyone and everyone.

Educated and intelligent receivers are more difficult to persuade through emotional appeals. Some studies have shown that personality factors like high neuroticism make people less responsive.

On the basis of a series of studies Janis found that people who are either over assertive or very submissive, and who are more inner directed are more difficult to persuade. On the other hand, those who are moderately aggressive and not inner directed, less intelligent, etc., are more easily persuaded. People with more imagination have been found to be more susceptible.

From the point of view of the source initiating communication, it is necessary to make sure that the receiver attends and receives the communication, comprehends it and understands what he or she is supposed to do. The communication should be clear, brief, yet adequate and above all, it should be able to hold the interest of the receiver and provide the necessary directions and information for action.

If this is not done, the message may not have the intended effects. If such messages are repeated, the consequence may be the emergence of general apathy or even resistance or reactance. It is also necessary to develop what may be called a communicating culture where people get accustomed to receiving and sending communications.

This is particularly true in instances where the target audience involves groups of people, continuous communication and who differ in many respects. In most instances of communication, particularly in organizations, the source may send across a communication and expect a return communication.

This is particularly true of work situations and organisations where reciprocal and even multilateral communications are involved. Choosing the appropriate time for communication is an important factor. If an important communication is sent to a large number of people towards the end of the day when they are tired, the message may not register on many people.

A common occurrence found in Indian organisations particularly government organisations is that communications are often sent to people who are not at all concerned. If such irrelevant communications are frequently sent, then even a relevant subsequent communication is likely to be ignored. For instance, governments letters are very long and the real message comes at the end, if at all there is one.

5. Feedback:

How does one come to know that one’s communication has been effective and has achieved the desired purpose? As may be evident, the effectiveness of communication depends on a number of factors. Further the degree of effectiveness varies from time to time and situation to situation.

An important point is that on most occasions, there is a scope for improving the effectiveness of communication particularly in organisations. This depends on the existence of a system to asses the degree of effectiveness of the communication process and making attempts to improve the communication.

A basic requirement here is the need to establish a system of feedback on the responses and reactions of the receivers on various aspects of the communication processes, the message, the channel, the clarity or overloading or under-loading, etc.

In organisations where there is a continuous process of communication it becomes absolutely essential to provide for a system of feedback at regular intervals. Similarly, whenever a new process of communication is to be initiated, it is better to test the same on an experimental scale and get the feedback.

The feedback may be obtained directly from the receiver or indirectly through others who are in a position to observe the receiver’s behaviour. Sometimes feedback may not be verbal and may be non­verbal. Organisations should have a system whereby as complete a feedback as possible is obtained.

6. Boomerang Effects :

Researchers on communication effectiveness have come across an interesting phenomenon called boomerang effect. It has been noticed that very often communication results in an effect which is exactly antithetical to the intended effect. This has been named the boomerang effect.

One factor which appears to be associated with this is the occurrence of a direct interaction or confrontation with a person towards whom the receive has a negative attitude. One investigation to identify this type of effect was that of Mansion. For example, if an individual “A” is trying to persuade a group of people to change their attitude or behaviour, and if another person “B” who is unpopular with the group happens to be there, then boomerang effect may occur.

Similarly, when attempts are made to change the attitudes of people who have strong negative attitudes in a sudden manner, we may witness the emergence of boomerang effect leading to hostility and total rejection of not only their message but even the source.

7. Sleeper Effect :

Sleeper effect is said to occur when the effect of the process of communication is not evident for a long time and then suddenly becomes evident. According to Hove land this happens when comprehension and assimilation of a message takes a long time, and depends on a process of consolidation which is time-consuming. This possibility is very likely where the communication is long and complex.

The sleeper effect appears to be more likely when an individual has a base of earlier attitudes which are fairly strong and have to be reviewed in the light of the communication that has been presented. But sleeper effect has not been found to occur very frequently.

Barriers to Communication:

Why is it that communication is effective on certain occasions and not on others? There are also certain other factors which influence the effectiveness of communication. One important factor is what is known as noise. While physical noise either at the source or at the receiver’s end, certainly can affect the effectiveness of communication, the term ‘noise’ means any irrelevant stimulation present at the same time or at the time of initiation of or receiving the communication.

Thus, if somebody conveys a message to you on the telephone, while you are busy studying or discussing something important with your friends, you are in a hurry and do not wait to understand the telephonic message clearly. Noise here means anything that does not permit you to attend to and listen to the message with full concentration and understand it completely and clearly, because at the moment you are engaged in some activity which is more important.

It may be seen here that it is not merely the factor of being physically engaged in some other activity, but even the psychological factor of ‘perceived importance’ and ‘interest’ play a crucial role. Apart from this, one may mention a factor known as “frame of reference”.

The source or the initiator may ask the receiver to do something or not to do something which according to the source may be important. But the receiver may not perceive this as important. Discrepancies in any of these between the source and the receiver may make the communication ineffective.

At the end of our discussion, perhaps the reader is in a position to appreciate the complexity of the process of communication and the various factors which influence the effectiveness of communication. But in-spite of all these, people communicate with each other across distances and on many matters. It is impossible to think of a world where people do not communicate.

This is an indication of importance of communication in our lives. One cannot imagine how people can live and live together, if communication processes are not so robust and function in-spite of the fact that many factors are involved in it. It is this ability to communicate which has made it possible for human beings to control and master the environment. The reader may wonder, why is communication so important!

It is important because communication serves many functions. Some of these are:

1. It helps us to express our ideas, feelings, reactions, hopes, etc. Pure verbal language may not serve this purpose in all situations of life. For example, children may not have acquired vocabulary to express their feelings and express through body language. In fact, the pet dog communicates its feelings very effectively.

2. To transmit or convey information to others.

3. To change the attitudes, behaviours and actions of others, and respond or reciprocate in such a way as to achieve something or accomplish something, reach a goal or target.

4. To establish bonds, relationship with others in terms of authority, control, camaraderie, etc.

5. To establish order and predictability in behaviour where a number of people are involved as in the case of organisations.

6. To hold together, a society, a group, or organization’s culture and values which are essential to provide meaning and usefulness to actions of people. Communication, therefore, is another most important function which differentiates living from the non-living and human from sub-human.

Today we know that very crucial developments in science and technology are taking place in the field of communication and the importance of such developments is very obvious. If one should think of any single distinct characteristic of contemporary human society, certainly it is communication. Modern society is a communicating society, though it may not be rational as Aristotle thought or pleasure-seeking as other philosophers thought.

An important development these days is the rapid advances in communication technology. The information or message can be passed on in no time. In fact, the occurrence of event and its awareness are almost simultaneous. Further today one often finds more emphasis on communication.

A few years ago one could not have thought of a teleconference or a continuous chat with an astronaut who is orbiting in the outer space. Communication, is probably the lifeline of society. This is now more true of modern society which is increasingly becoming an information based society.

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Learning the language of nature

Timothy seekings.

essay about nature of language

Nature has brought forth intelligences, minds, and languages. We need to come to our senses and begin to listen.

Art, spirituality and epidemiology do not often intersect. But when they do, it’s worth taking a closer look, especially when the emerging perspective can help us respond to epidemics such as Covid-19 in a more constructive manner.

The current global health crisis is an invitation to listen and reflect on the varying consequences of different kinds of collective human action and the impact of wider social and economic structures. 

The inspiration for this way of seeing originates in a recent documentary by artist James Bridle titled  Se ti sabir , the essay ' Spirituality as Common Sense ' by Brother David Steindl-Rast, and the idea of a “ language of epidemics ” suggested by epidemiologist David-Waltner Toews.

Communication 

Bridle recounts how between the eleventh and the nineteenth centuries, lingua franca served as the pidgin language around the Mediterranean, facilitating trade, travel, and communication between members of different nations.

In this language, the word “sabir” meant “to know”. It was commonly used as a question, in which case it meant: “do you know lingua franca, can we speak?” At first encounters, people would greet each other by asking “sabir?” This was a greeting and, at the same time, a means to determine whether they spoke the common language.

Today, as artificial intelligence becomes increasingly sophisticated, we are also revealing other intelligences in nature, in plants and animals and in ecological communities at all scales. In other words, we are gradually realizing that humans are one of multiple forms of knowledge on this living planet.

Bridle offers this example: The eye evolved independently, twice – once in the class of cephalopods, and a second time, in vertebrates. He suggests that the mind, too, has evolved more than once independently, in different species. Octopuses (cephalopods), for instance, are among the most intelligent and behaviourally diverse of all invertebrates.

The idea then is that mind is not an exclusively human feature. There are other minds around us in nature, emerging out of different forms of life. And this invites back the idea of a common language. Can we speak? Can we communicate with those minds?

Brother David Steindl-Rast provides a tentative answer in his essay  Spirituality as Common Sense .  In his view, common sense does not refer to rational thinking and problem solving in practical matters. Rather, it is comparable to the basic human senses such as sight, hearing, smell, taste, and touch, which allow us to connect to the world around us.

But senses are not exclusive to humans either. All life connects to the surrounding world, and Steindl-Rast suggests that it does so through the common sense.

As humans, we experience this common sense when we connect with animals, trees, or other living beings and experience moments of affinity and deep connectedness.  

What if life has a common language? We know that all life shares a common code: DNA and RNA. But what if life has a common language of the senses, not a language based on a system of semiotics, but one based on a common sense, a language that we can understand in principle, perhaps through feeling? 

The current case of Covid-19 has to be understood in the context of other infectious diseases that have emerged in recent decades, including MERS, SARS, and H5N1. David Waltner-Toews elaborates on how these outbreaks are not random occurrences but the consequences of human actions and inactions as well as dominant social and economic structures. In this sense, these outbreaks have to be understood as “complex messages from the natural world”. 

A case in point is our proximity to livestock. In the same way as the invention of the ship inadvertently introduced the shipwreck, the domestication of animals in human history brought with it zoonosis, the leaping of pathogens from other animal species to the human.

Many of the most harmful diseases that humans have faced throughout history originate from livestock. Among the diseases to emerge in recent decades, 70 percent are of animal origin. 

After pathogens make the leap to humans, they encounter conducive breeding grounds: modern, dense yet atomised societies, in which the idea of healthcare has come to denote a system for treating diseases rather than one for nurturing healthy people and communities.

In 2008 the WHO determined that the main social determinant of health was social injustice, yet income gaps continue to expand, and unequal distribution of power, money, and resources persist. 

Extraction 

Dominant economic thinking is growth-oriented, exploitative, extractive, resource-hungry, short-sighted, and polluting. Urban centres live off the periphery, the periphery lives off the natural resources.

The relentless expansion of extractive industries feeding the global economy pushes already marginalised people further into the margins and increases contact with the last vestiges of wildlife.

Primary forests, protected areas, and wildlife are not just the linchpins of essential life support systems, but also repositories of unknown scale when it comes to pathogens such as ancient bacteria and viruses. Bringing humans, livestock, trade, and travel closer to them is like extending a welcoming invitation and creating zoonotic passageways for pathogens to the human species.

Newly emerging epidemics are therefore not freak occurrences that will simply go away again. They emerge as consequences of extractive action. They are the outcomes of systemic processes, pressures, relationships, and structures that are innate to the modern global (economic) system and modern lifestyles.

As such, they tell us something about our human behaviour and about our ideas. And therefore, they have to be understood as messages from the realm of nature. Decades ago, ethnobotanist Terrence Mckenna stated that “nature is alive and talking to us, and this is not a metaphor.” Waltner-Toews shows how this can be understood in relation to epidemics. 

If nature is alive and talking to us, it has been sending us a lot of messages. Therefore, it is high time to think about life’s lingua franca, this common sense shared by living beings.

Humans aren’t the only animals that have evolved intelligences, minds, and languages. The unfolding of life has been going on for millions of years. Planet Earth is the home of between 1.2 million known and 8.7 million unknown species of life. From the smallest, to the largest proponents, life has a common code and a common sense.

If these messages have been falling on deaf ears, it might be because we are generally blind to what we cannot see and deaf to what we cannot hear. The question is: Are there intelligent lifeforms that we don’t recognize as such? Are there minds and languages around us that we do not see because, as George Berkeley suggested, the only things we perceive are our  own  perceptions?

In other words, we preclude the possibility of intelligent life, other minds, and other languages of life through our systems of thought and semantic definitions. As modern proponents of science, we have for most part insisted that our interlocuters from the living world speak our language, the language of reason and intellect.

New literacy

As humans we encounter a host of life forms. We encounter hyperobjects as large as typhoons, global warming, and Gaia (12,742 km) and entities as small as Covid-19 (120 nm). The take-away message is that our encounters with these life forms are not freak events occurring in a random universe for no reason. 

These encounters are transmissions of messages. They are not messages in the way you receive a text message or an email. These encounters are messages more in the sense of Marshall McLuhan’s work, captured in his catch phrase “the medium is the message".

They are messages because they represent “a change of scale or pace or pattern“ that is introduced into human affairs, and because they reflect the consequences of our collective behaviour. They are feedback to our actions, like when you hold your finger over a flame. The pain is the feedback. The pain is also the message. The message is pull away your finger. 

In the same way, if you continue to expand the sacrifice zones of your extractive industries, displace the already marginalized and disadvantaged human populations and stir up wildlife populations by disturbing their habitats; if you maintain high-density population livestock, increase bacterial resistance by over-applying antibiotics in agriculture; if you create a ‘flexible’ work force, encourage mass consumer society and provide the global infrastructure for international trade, travel, and transport, etc.

Well, as they say, you don’t always get what you pay for, but you always pay for what you get. In other words, we reap what we sew and we get to experience the outcomes of our actions. Covid-19 is one such outcome, one such incoming message. 

Recent work and literature suggests that other intelligences exist around us. These represent other minds and other languages. In addition, as life has a common code in the form of DNA and RNA, it exhibits a common sense.

Emerging infectious diseases as well as other global and environmental phenomena are not just random occurrences, but rather complex messages from nature. However, we modern humans have largely failed to perceive those encounters as messages because we don’t understand the language.

Nature’s preamble to all our incoming messages is ‘sabir?’ Do you know? Do you speak the common language? And our answer has mostly been ‘No, speak ours!’ The latest epidemic really brings home the message: We should start to learn this common language. Most cultures in human history probably spoke this language to some extent, and it is still spoken today. And that’s good. It means we can find teachers.

In this sense, the task is clear. We have to become multilingual, and that means learning the lingua franca of the living planet. In the Anthropocene, our most pressing concern is to be able to hear and understand what she has to say, because we have been ignoring her messages for too long.

Therefore, in the future, when we encounter an incoming message, we will hopefully be able to respond as we should do: Sabir. We know. Talk. We are listening.  

This Author

Timothy Seekings is a PhD student in Natural Resources and Environmental Science at National Dong Hwa University, Taiwan. His research is about edible insects and his most recent publication is  The proof is in the cricket: engaging with edible insects through action research. 

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Nature Essay for Students and Children

500+ words nature essay.

Nature is an important and integral part of mankind. It is one of the greatest blessings for human life; however, nowadays humans fail to recognize it as one. Nature has been an inspiration for numerous poets, writers, artists and more of yesteryears. This remarkable creation inspired them to write poems and stories in the glory of it. They truly valued nature which reflects in their works even today. Essentially, nature is everything we are surrounded by like the water we drink, the air we breathe, the sun we soak in, the birds we hear chirping, the moon we gaze at and more. Above all, it is rich and vibrant and consists of both living and non-living things. Therefore, people of the modern age should also learn something from people of yesteryear and start valuing nature before it gets too late.

nature essay

Significance of Nature

Nature has been in existence long before humans and ever since it has taken care of mankind and nourished it forever. In other words, it offers us a protective layer which guards us against all kinds of damages and harms. Survival of mankind without nature is impossible and humans need to understand that.

If nature has the ability to protect us, it is also powerful enough to destroy the entire mankind. Every form of nature, for instance, the plants , animals , rivers, mountains, moon, and more holds equal significance for us. Absence of one element is enough to cause a catastrophe in the functioning of human life.

We fulfill our healthy lifestyle by eating and drinking healthy, which nature gives us. Similarly, it provides us with water and food that enables us to do so. Rainfall and sunshine, the two most important elements to survive are derived from nature itself.

Further, the air we breathe and the wood we use for various purposes are a gift of nature only. But, with technological advancements, people are not paying attention to nature. The need to conserve and balance the natural assets is rising day by day which requires immediate attention.

Get the huge list of more than 500 Essay Topics and Ideas

Conservation of Nature

In order to conserve nature, we must take drastic steps right away to prevent any further damage. The most important step is to prevent deforestation at all levels. Cutting down of trees has serious consequences in different spheres. It can cause soil erosion easily and also bring a decline in rainfall on a major level.

essay about nature of language

Polluting ocean water must be strictly prohibited by all industries straightaway as it causes a lot of water shortage. The excessive use of automobiles, AC’s and ovens emit a lot of Chlorofluorocarbons’ which depletes the ozone layer. This, in turn, causes global warming which causes thermal expansion and melting of glaciers.

Therefore, we should avoid personal use of the vehicle when we can, switch to public transport and carpooling. We must invest in solar energy giving a chance for the natural resources to replenish.

In conclusion, nature has a powerful transformative power which is responsible for the functioning of life on earth. It is essential for mankind to flourish so it is our duty to conserve it for our future generations. We must stop the selfish activities and try our best to preserve the natural resources so life can forever be nourished on earth.

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2024 LIFE - Calls for proposals

Life calls for proposals 2024.

The LIFE Calls for proposals 2024 are expected to be published on the  Funding & tender opportunities portal  on 18 April 2024. 

CINEA will hold virtual information sessions from 23 to 26 April 2024 to guide potential applicants through the LIFE Calls for proposals 2024. See the  LIFE YouTube channel  for previous  recordings .  

Anticipated submission deadlines

Standard Action Projects (SAPs) for circular economy and quality of life Deadline date: 19 September 2024 

Standard Action Projects (SAPs) for nature and biodiversity Deadline date: 19 September 2024 

Standard Action Projects (SAPs) for climate change mitigation and adaptation Deadline date: 17 September 2024   

Coordination and Support Action Grants (CSA) for clean energy transition sub-programme Deadline date: 19 September 2024

  • Concept notes:  Deadline date: 5 September 2024 
  • Full proposals:  Deadline date: 6 March 2025  

Technical Assistance preparation for SIPs and SNAPs: Deadline date: 19 September 2024    

Technical Assistance Replication Deadline date: 19 September 2024  

Framework Partnership Agreements (FPA OG)  Deadline date: 5 September 2024

Specific Operating Grant Agreements (SGA OG)  Deadline date: 17 September 2024 

LIFE Preparatory Projects (addressing ad hoc Legislative and Policy Priorities - PLP) Deadline date: 19 September 2024 

Type of grants

Standard action projects (sap).

Projects, other than strategic integrated projects, strategic nature projects or technical assistance projects, that pursue the specific objectives of the LIFE programme.

Strategic Nature Projects (SNAP)

Projects that support the achievement of Union nature and biodiversity objectives by implementing coherent programmes of action in Member States in order to mainstream those objectives and priorities into other policies and financing instruments, including through coordinated implementation of the prioritised action frameworks adopted pursuant to Directive 92/43/EEC.

Strategic Integrated Projects (SIP)

Projects that implement, on a regional, multi-regional, national or transnational scale, environmental or climate strategies or action plans developed by Member States' authorities and required by specific environmental, climate or relevant energy legislation or policy of the Union, while ensuring that stakeholders are involved and promoting coordination with and mobilisation of at least one other Union, national or private funding source.

Technical Assistance Projects (TA)

Projects that support the development of capacity for participation in standard action projects, the preparation of strategic nature projects and strategic integrated projects, the preparation for accessing other Union financial instruments or other measures necessary for preparing the upscaling or replication of results from other projects funded by the LIFE programme, its predecessor programmes or other Union programmes, with a view to pursuing the LIFE programme objectives set out in Article 3; such projects can also include capacity-building related to the activities of Member States' authorities for effective participation in the LIFE programme.

Other Action Grants (OAG)

Actions needed for the purpose of achieving the general objective of the LIFE programme, including coordination and support actions aimed at capacity-building, at dissemination of information and of knowledge, and at awareness-raising to support the transition to renewable energy and increased energy efficiency.

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Grants that support the functioning of non-profit making entities which are involved in the development, implementation and enforcement of Union legislation and policy, and which are primarily active in the area of the environment or climate action, including energy transition, in line with the objectives of the LIFE programme.

Project proposals submitted under LIFE calls are evaluated and scored against selection and award criteria.

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Guidance on the application process, evaluation and grant signature, and working as an expert will be available on the Funding & Tender portal .

Please see our dedicated page on Support for Applicants.

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  • Published: 21 February 2024

Extracting accurate materials data from research papers with conversational language models and prompt engineering

  • Maciej P. Polak   ORCID: orcid.org/0000-0001-7198-7779 1 &
  • Dane Morgan   ORCID: orcid.org/0000-0002-4911-0046 1  

Nature Communications volume  15 , Article number:  1569 ( 2024 ) Cite this article

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  • Theory and computation

There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they require a significant amount of up-front effort, expertise, and coding. In this work, we propose the ChatExtract method that can fully automate very accurate data extraction with minimal initial effort and background, using an advanced conversational LLM. ChatExtract consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract that data, and assure the data’s correctness through a series of follow-up questions. These follow-up questions largely overcome known issues with LLMs providing factually inaccurate responses. ChatExtract can be applied with any conversational LLMs and yields very high quality data extraction. In tests on materials data, we find precision and recall both close to 90% from the best conversational LLMs, like GPT-4. We demonstrate that the exceptional performance is enabled by the information retention in a conversational model combined with purposeful redundancy and introducing uncertainty through follow-up prompts. These results suggest that approaches similar to ChatExtract , due to their simplicity, transferability, and accuracy are likely to become powerful tools for data extraction in the near future. Finally, databases for critical cooling rates of metallic glasses and yield strengths of high entropy alloys are developed using ChatExtract .

Introduction

Automated data extraction is increasingly used to develop databases in materials science and other fields 1 . Many databases have been created using natural language processing (NLP) and language models (LMs) 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 . Recently, the emergence of large language models (LLMs) 23 , 24 , 25 , 26 , 27 has enabled significantly greater ability to extract complex data accurately 28 , 29 . Previous automated methods require a significant amount of effort to set up, either preparing parsing rules (i.e., pre-defining lists of rules for identifying relevant units or particular phrases that identify the property, etc.), fine-tuning or re-training a model, or some combination of both, which specializes the method to perform a specific task. Fine-tuning is resource and time consuming and requires extensive preparation of training data, which may not be accessible to the majority of researchers. With the emergence of conversational LLMs such as ChatGPT , which are broadly capable and pretrained for general tasks, there are opportunities for significantly improved information extraction methods that require almost no initial effort. These opportunities are enabled by harnessing the outstanding general language abilities of conversational LLMs, including their inherent capability to perform zero-shot (i.e., without additional training) classification, accurate word references identification, and information retention capabilities for text within a conversation. These capabilities, combined with prompt engineering , which is the process of designing questions and instructions (prompts) to improve the quality of results, can result in accurate data extraction without the need for fine-tuning of the model or significant knowledge about the property for which the data is to be extracted.

Prompt engineering has now become a standard practice in the field of image generation 30 , 31 , 32 to ensure high quality results. It has also been demonstrated that prompt engineering is an effective method in increasing the accuracy of reasoning in LLMs 33 .

In this paper we demonstrate that using conversational LLMs such as ChatGPT in a zero-shot fashion with a well-engineered set of prompts can be a flexible, accurate, and efficient method of extraction of materials properties in the form of the triplet Material, Value, Unit . We were able to minimize the main shortcoming of these conversational models, specifically errors in data extraction (e.g., improperly interpreted word relations) and hallucinations (i.e., responding with data not present in the provided text), and achieve 90.8% precision and 87.7% recall on a constrained test dataset of bulk modulus, and 91.6% precision and 83.6% recall on a full practical database construction example of critical cooling rates for metallic glasses. These results were achieved by identifying relevant sentences, asking the model to extract details about the presented data, and then checking the extracted details by asking a series of follow-up questions that suggest uncertainty of the extracted information and introduce redundancy. This approach was first demonstrated in a preprint of this paper 34 , and since then a group from Microsoft has described a similar idea, but for more general tasks than materials data extraction 35 . We work with short sentence clusters made up of a target sentence, the preceding sentence, and the title, as we have found these almost always contain the full Material, Value, Unit triplet of data we seek. We also separated cases with single and multiple data values in a sentence, thereby greatly reducing certain types of errors. In addition, by encouraging a certain structure of responses we simplified automated post-processing the text responses into a useful database. We have put these approaches together into a single method we call ChatExtract —a workflow for a fully automated zero-shot approach to data extraction. We provide an example ChatExtract implementation in a form of a python code (see “Data availability” for more details). The prompt engineering proposed here is expected to work for essentially all Material, Value, Unit data extraction tasks. For different types of data extraction this prompt engineering will likely need to be modified. However, we believe that the general method, which is based on simple prompts that utilized uncertainty-inducing redundant questioning applied within an information retaining conversational model, will provide an effective and efficient approach to many types of information extraction.

The ChatExtract method is largely independent of the conversational LLM used and is expected to improve as the LLMs improve. Therefore, the astonishing rate of LLM improvement is likely to further support the adoption of ChatExtract and similar approaches to data extraction. Prompt engineering has now become a standard practice in the field of image generation 30 , 31 , 32 to ensure high quality results. A parallel situation may soon occur for data extraction. Specifically, a workflow such as that presented here with ChatExtract , which includes prompt engineering utilized in a conversational set of prompts with follow-up questions, may become a method of choice to obtain high quality data extraction results from LLMs.

Results and discussion

Description of the data extraction workflow.

Figure  1 shows a simplified illustration of the ChatExtract workflow. The full workflow with all of the steps is shown in Fig.  2 so here we only summarize the key ideas behind this workflow. The initial step is preparing the data and involves gathering papers, removing html/XML syntax and dividing into sentences. This task is straightforward, standard for any data extraction effort, and described in detail in other works 29 .

figure 1

Only the key ideas for each of the steps are shown, with the fully detailed workflow presented in Fig.  2 .

figure 2

Blue boxes represent prompts given to the model, gray boxes are instructions to the user, “Yes”, “No”, and “None” boxes are model’s responses. The bold text in “[]” are to be replaced with appropriate values of the named item, which includes one of sentence (the target sentence being analyzed); text (the expanded text consisting of Title, Preceding sentence, and target sentence); name of the property; extracted material, value, or unit.

The data extraction is done in two main stages:

Initial classification with a simple relevancy prompt, which is applied to all sentences to weed out those that do not contain data.

A series of prompts that control the data extraction from the sentences categorized in stage (A) as positive (i.e., as relevant to the materials data at hand). To achieve high performance in Stage (B) we have developed a series of engineered prompts and the key Features of the major Stage (B) are summarized here:

Split data into single- and multi-valued, since texts containing a single entry are much more likely to be extracted properly and do not require follow up prompts, while extraction from texts containing multiple values is more prone to errors and requires further scrutinizing and verification.

Include explicitly the possibility that a piece of the data may be missing from the text. This is done to discourage the model from hallucinating non-existent data to fulfill the task.

Use uncertainty-inducing redundant prompts that encourage negative answers when appropriate. This lets the model reanalyze the text instead of reinforcing previous answers.

Embed all the questions in a single conversation as well as representing the full data in each prompt. This simultaneously takes advantage of the conversational information retention of the chat tool while each time reinforcing the text to be analyzed.

Enforce a strict Yes/No format of answers to reduce uncertainty and allow for easier automation.

Stage (A) is the first prompt given to the model. This first prompt is meant to provide information whether the sentence is relevant at all for further analysis, i.e., whether it contains the data for the property in question (value and units). This classification is crucial because, even in papers that have been extracted to be relevant by an initial keyword search, the ratio of relevant to irrelevant sentences is typically about 1:100. Therefore elimination of irrelevant sentences is a priority in the first step. Then, before starting Stage (B), we expand the text on which we are operating to a passage consisting of three sentences: the paper’s title , the sentence preceding the positively classified sentence from the previous prompt, and the positive sentence itself. This expansion is primarily useful for making sure we include text with the material’s name, which is sometimes not in the sentence targeted in Stage (A) but is most of the time in the preceding sentence or title. While in some cases the text passage built this way may not contain all the information to produce a complete datapoint, for example if the materials name is mentioned earlier in the text or in a subsection where samples are described, we found this to be a relatively rare occurrence. While technically expanding the passage to ensure extraction of complete datapoints is possible, we found that operating on as short of a text passage as possible results in the most accurate extraction, and the small gain in recall from expanding the text passage was not worth the cost of loss of precision of overall extraction. That said, tuning of the text selection approach to different LLMs and/or target properties could likely achieve improvements in some cases.

The relevant texts vary in their structure and we found it necessary to use different strategies for data extraction for those sentences that contain a single value and those sentences that contain multiple values (Feature (1) above). The texts containing only a single value are much simpler since the relation between material, value, and unit does not need to be analyzed. The LLMs tend to extract such data accurately and a single well-engineered prompt for each of the fields asked only once tends to perform very well. Texts containing multiple values involve a careful analysis of the relations between words to determine which values, materials, and units correspond to one another. This complexity sometimes leads to errors in extraction or hallucinated data and requires further scrutiny and prompting with follow-up questions. Thus the first prompt in Stage (2) aims at determining whether there are multiple data points included in a given sentence, and based on that answer one of two paths is taken, different for single-valued and multi-valued sentences. As a concrete example of how often this happens, our bulk modulus dataset studied below has 70% multi-valued and 30% single-valued sentences. Our follow-up question approach proved to be very successful for the conversational ChatGPT models.

Next, the text is analyzed. For a single-valued text, we directly ask questions about the data in the text, asking separately for the value, its unit, and the material’s name. It is important to explicitly allow for an option of a negative answer (Feature (2) above), reducing the chance that the model provides an answer even though not enough data is provided, limiting the possibility of hallucinating the data. If a negative answer is given to any of the prompt questions, the text is discarded and no data is extracted. For the case of a multi-valued sentence, instead of directly asking for data, we ask the model to provide structured data in a form of a table. This helps organize the data for further processing but can produce factually incorrect data, even if explicitly allowing negative responses. Therefore, we scrutinize each field in the provided table by asking follow-up questions (this is the redundancy of Feature (3) above) whether the data and its referencing is really included in the provided text. Again, we explicitly allow for a negative answer and, importantly, plant a seed of doubt that it is possible that the extracted table may contain some inaccuracies. Similarly as before, if any of the prompt answers are negative, we discard the sentence. It is important to notice that despite the capability of the conversational model to retain information throughout the conversation, we repetitively provide the text with each prompt (Feature (4) above). This repetition helps in maintaining all of the details about the text that is being analyzed, as the model tends to pay less attention to finer details the longer the conversation is continued. The conversational aspect and information retention improves the quality of the answers and reinforces the format of short structured answers and possibility of negative responses. The importance of the information retention in a conversation is proven later in this work by repeating our analysis exercise but with a new conversation started for each prompt, in which cases both precision and recall are significantly lowered. It is also worth noticing that we enforce a strictly Yes or No format of answers for follow up questions (Feature (5) above), which enables automation of the data extraction process. Without this constraint the model tends to answer in full sentences which are challenging to automatically analyze.

The prompts described in the flowchart (Fig.  2 ) are engineered by optimizing the accuracy of the responses through trial and error on various properties of varying complexity. Obviously, we have not exhausted all options, and it is likely that further optimization is possible. We have, however, noticed that contrary to intuition, providing more information about the property in the prompt usually results in worse outcomes, and we believe that the prompts proposed here are a reliable and transferable set for many data extraction tasks.

Performance evaluation and model comparison

We have investigated the performance of the ChatExtract approach on multiple property examples, including bulk modulus, metallic glass critical cooling rate, and high-entropy alloy yield stress. For bulk modulus the data is highly restricted so we can collect complete statistics on performance, and the other two cases represent applications of the method to full database generation. The bulk modulus test dataset has been chosen as a representative and particularly demanding test case for several reasons. Papers investigating mechanical properties, such as bulk modulus, very often report other elastic properties, such as the Young’s modulus or shear modulus, which have similar names, ranges of values, and the same units of pressure, and are therefore easy to confuse with bulk modulus. In addition, those source documents very often describe measurements performed under pressure and other forms of stress, which have the same pressure units as bulk modulus. Finally, bulk modulus data is very often accompanied with information on the derivative of bulk modulus, which is easily confused as well. Therefore, the bulk modulus serves as a test example in which the sought property is often presented alongside numerous other, irrelevant but very easily mistakable values, presenting a challenge for accurate extraction. Our bulk modulus example data is taken from a large body of sentences extracted from hundreds of papers with bulk modulus data. To allow for an effective assessment we wanted a relatively small number of relevant sentences (containing data) of around a 100, from which we could manually extract to provide ground truth. We manually extracted data until we reached 100 relevant sentences, during which a corresponding number of 9164 irrelevant sentences (not containing data) was also labeled. We then post-processed the irrelevant sentences to remove ones that do not contain any numbers with a simple regular expression to obtain 1912 irrelevant sentences containing numbers. This preserves resources and saves time by not running the language model on sentences that obviously do not contain any data at all (since the values to be extracted are numbers), and does not impact the results of the assessment, as in our extensive tests the model never returns any datapoints from sentences that do not contain numbers. The model is explicitly instructed not to do so in the prompts (see Fig.  2 ), even if it mistakenly classifies the sentence as relevant in the very first classification prompt (which is very rarely the case for sentences without numbers). In these 100 sentences with data, there were a total of 179 data points (a complete triplet of material, bulk modulus, and unit combination), which we extracted by hand to serve as a ground truth dataset. We investigated the performance of multiple versions of ChatGPT models (see Table  1 ) by following the approach as described above and in Fig.  2 . For true positive sentences we divide the results into categories by type of text: single-valued and multi-valued, and provide the overall performance over the entire dataset. These results are summarized in Table  1 . Note that single- and multi-values columns represent performance on input passages that have data, which is of interest for understanding model behavior. The statistic that best represents the model performance on real sentences is the overall column, where the input contains sentences both with and without data. We applied what we consider to be quite stringent criteria for assessing the performance against ground truth, the details of which an be found in the “Methods” section.

The best LLM ( ChatGPT-4 ) achieved 90.8% precision at 87.7% recall, which is very impressive for a zero-shot approach that does not involve any fine-tuning. Single-valued sentences tend to be extracted with slightly higher recall (100% and 85.5% in ChatGPT-4 and ChatGPT-3.5 , respectively) compared to multi-valued sentences with a recall of 82.7% and 55.9% for the same models.

We believe that there are two core features of ChatGPT that are being used in ChatExtract to make this approach so successful. The first feature, and we believe the most important one, is the use of redundant prompts that introduce the possibility of uncertainty about the previously extracted data. By engineering the set of prompts and follow-up questions in this way, they substantially improve the factual correctness, and therefore precision, of the extracted information. The second feature is the conversational aspect, in which information about previous prompts and answers is retained. This allows the follow-up questions to relate to the entirety of the conversation, including the model’s previous responses.

In order to demonstrate that the follow-up questions approach is essential to the good performance we repeated the exercise for both ChatGPT models without any follow-up questions (directly asking for structurized data only, in the same manner as before, only without asking the follow-up prompts in the multi-value branch (long light green box on right side of Fig.  2 )). The results are denoted as (no follow-up) in Table  1 . The dominant effects of removing follow-up questions is to allow more extracted triplets to make it to the final extracted database. This generally increases recall across all cases (single-valued, multi-valued, and overall). For passages with data (single-valued, multi-valued) these additional kept triplets are very few and almost all correct, leading to just slightly lower precisions. However, for the large number of passages with no data the additional kept triplets represent many false positives, and therefore dramatically reduce precision in the overall category. Specifically, removing follow-up questions decreases the overall precision to just 42.7% and 26.5% for ChatGPT-4 and ChatGPT-3.5 , respectively, from the values of 90.8% and 70.1%, respectively, resulting from a full ChatExtract workflow. These large reductions in precision demonstrate that follow-up questions are critical, and the analysis shows that their role is primarily to avoid the model erroneously hallucinating data in passages where none was actually given.

In order to demonstrate that the information retention provided by the conversational model is important to the good performance we repeated our approach but modified it to start a new conversation for each prompt, which meant that no conversation history was available during each prompt response. The results are denoted (no chat) in Table  1 , This test was performed on ChatGPT-3.5 and had little or no reduction in precision. However, there was a significant loss in recall in all categories (e.g., overall recall dropped by 10.7% to 54.7%). This loss is recall is because the multiple redundant questions tend to reject too many cases of correctly extracted triplets when the questions are asked without model knowing they are connected through a conversation. We did not perform this test on ChatGPT-4 to reduce overall time and expense as the implications results on ChatGPT-3.5 seemed clear.

While currently the OpenAI GPT models, in particular GPT4, are considered to be the most capable and are the most widely used, the fact that they are entirely proprietary, with a limited access dependent on OpenAI servers, and of limited transparency on their internal workings. Their default versions also tend to change their performance over time 36 , which we overcome by using version snapshots (see “Methods” section), however there is no guarantee for their availability in the future. As an alternative model to assess, we chose LLaMA2-chat (70B) 37 , a model developed by GenAI (Meta), which has extensive documentation 38 , and is available to download for free and use locally without limits. The performance of the LLaMA2-chat model is summarized alongside other models in Table  1 , where an overall precision and recall of 61.5 and 62.9% was achieved. The performance is close, but slightly worse than that of ChatGPT-3.5 , which is expected based on the overall assessment of LLaMA2 capabilities 27 . While the ChatGPT-4 model is still the most capable and performs with significantly better outcomes, this demonstrates that alternative models are also capable of data extraction, and their accuracy is likely to improve as they catch up to the state-of-the-art. It is worth noting that although the method and the prompts have been developed to be general and applicable to any LLM, the prompts have been optimized based on GPT models. Therefore, it is possible that further prompt optimization could improve performance for a specific LLM, such as LLaMA2-chat presented here, making the comparison not entirely fair for LLaMA2-chat. However, we do not expect this effect to be very significant. In order to compare the performance of ChatExtract to previous state-of-the-art data extraction methods, we performed an assessment of the performance of ChemDataExtractor2 (CDE2) on our test bulk modulus dataset. CDE2 requires, at minimum, a specifier expression and units to be explicitly specified. The specifier expression used here included all the ways we found the bulk modulus is addressed in our test data: “bulk modulus”, “B”, “B0”, “B_0”, “K”, “K0”, and “K_0”. We also created a new unit type for units of pressure, which included all units we encountered in our test data: “GPa”, “MPa”, “Pa”, “kbar”, and “bar”. CDE2 was then ran on the same text passages from our bulk modulus dataset as ChatExtract. The overall precision and recall were found to be 57% and 31% respectively, slightly lower but close to the low range results reported for thermoelectric properties (78% and 31%, respectively) obtained in ref. 6 by the authors of CDE2. We note that in this paper we use a more strict definition for a false negative datapoint than the authors of CDE2, which results in a slightly lower recall. Even though the performance of ChatExtract is better, it is worth noting that CDE2 can be efficiently executed on a personal computer with a single CPU, while the use of LLMs at the time of writing this article requires significantly higher computational power.

Application to tables and figures

Data is not necessarily always contained within the text of the paragraph, and may be found in other structures, in particular in tables and figures. Since tables already contain structured datapoints, LLMs can certainly assist in their efficient extraction from the document. The analysis of figures, on the other hand, is not a language processing task, and is an ongoing challenge for machine learning and artificial intelligence. LLMs can, however, help identify relevant figures for further human analysis. Figure  3 shows workflows for tables (1) and figures (2). Here, we utilize a simple workflow for table extraction—tables and their captions are gathered separately from the texts of the papers, and then they are used in classification, in a similar fashion to sentences (first step in the general ChatExtract workflow, Fig.  2 to determine whether they do contain the relevant data or not. In the case of positive classification, the text of the table and its caption are provided to the LLM and the model is instructed to only extract the relevant data for the specified property, in the form of a table, in the same way as in the general ChatExtract workflow. This step ensures that only the relevant data is extracted, as tables often contain more than just one column or one property and have to be further postprocessed. Since the data is already structured and the probability of an incorrect extraction is low, the redundant follow-up verification does not seem to be helpful and is not performed, similar to our approach for sentences for single values. For figures, only the figure caption is used in the classification, where In the case of a positive classification of a figure caption, the figure is downloaded for later manual data extraction. The accuracy for table extraction using the model which performed best for text extraction (GPT4) is quite high, as extracting structured data from an already structured table poses fewer challenges than extraction from texts. Out of 163 tables contained in the same papers which served as a source for the text bulk modulus data, we manually classified 58 as containing bulk modulus data. From these tables we were able to manually extract 500 structured bulk modulus datapoints. Using ChatExtract we were able to achieve a precision and recall for table classification of 95% and 98%, respectively. The precision and recall when counting structured data extraction for individual datapoints reached 91% and 89%, respectively. The lowering of the statistics, besides the consequence of the sporadic improper classification, was almost entirely due to an improper formatting of tables when converted from XML to raw text. While it did not happen very often, in the cases when it did, it was impossible for humans to extract data from these wrongly formatted tables as well. Even though these are not directly the method’s fault, they are still counted as false positives and false negatives in our assessment, as they will inevitably be present in the final extracted database, and this is what ultimately matters the most. Assessment of accuracy for figure classification is more difficult, as figures usually present more complex data than the simple “material, value, unit” triplets we discuss here. Therefore the criterion for a successful classification was whether the figure contained the relevant property on any of the axes, in the legend, written somewhere in the figure, or in the caption itself. Out of 436 figures contained in the same papers which served as a source for the text bulk modulus data, we manually classified 45 as containing bulk modulus data. Using the model which performed best for text extraction (GPT4) we found a 82% recall and 80% precision for the figure relevancy classification. While these results are very encouraging, it is worth noting that this is not full data extraction from figures, which is a very challenging task overall. In the case of our test bulk modulus data, for example, the bulk modulus was often contained in the pressure or energy as a function of volume plots as one of the parameters in the fitted equation of state, simply written next to the curve, while the figure caption describing the figure only says that it contains the pressure or energy as a function of volume. While a human with knowledge in the field knows that such figures represent equations of state and bulk modulus is one of the parameters in the equation of state and may expect its value in such a plot, which even a human without expertise would not be able to do. Nevertheless, in our evaluation, we considered such figures as relevant and containing data, which negatively impacted the recall. Interestingly most of the reduction in precision came from a similar reason—the figure would be explicitly captioned as containing a fitted equation of state curve, and a model would classify such a figure positively (since bulk modulus is the key parameter in the fitting) yet the figure would not directly contain the bulk modulus data.

figure 3

Panel ( a ) contains a flowchart for extracting data from tables while panel ( b ) contains a flowchart for extracting data from figures. Blue boxes represent prompts given to the model, gray boxes are instructions to the user, “Yes”, “No” boxes are model’s responses. The bold text in “[]” are to be replaced with appropriate values of the named item.

To further demonstrate the utility of the ChatExtract approach we used the method to extract two materials property databases, one for critical cooling rates of bulk metallic glasses and one for yield strength of high entropy alloys. Before sharing the results of these data extractions it is useful to consider in more detail different types of desirable database of a materials property that might be extracted from text.

Different types of databases can be achieved with different levels of post-processing after automated data extraction. Here we describe three types of databases that we believe cover most database use cases. At one extreme is a database that encompasses all relevant mentions of a specific property, which is useful when initiating research in a particular field to assess the extent of data available in the literature, the span of values (including outliers), and the various material groups investigated. Entries included in such database might contain ranges, limits or approximate values, families of materials, etc. This is typically what the ChatExtract directly extracts, and we will refer to this as the raw database. At the other extreme is a strict standardized database, which contains uniquely defined materials with standard format, machine-readable compositions, and discrete values (i.e., not ranges or limits) with standardized units (which also helps remove the very rare occurrence where a triplet with wrong units is extracted). A standardized database facilitates easy interpretation and usage by computer codes and might be well-suited to, e.g., machine learning analysis. A standardized database can be developed from a raw data collection and will be a subset of that raw data. A third type of dataset, which is intermediate between raw and standardized , is a cleaned database, which removes duplicate triplets derived from within a single paper from the raw data, as these are almost always the exact same entry repeated multiple, e.g., in the “Discussion and Conclusions” sections) and are obviously undesirable. While the cleaned database can be done automatically, the standardized database may require some manual post-processing of the data extracted with the ChatExtract method.

Results of real-life data extraction

In this study we provide two materials property databases: critical cooling rates of metallic glasses, and yield strength of high entropy alloys. Both databases are presented in all three of the above forms: raw data which is what is directly extracted by the ChatExtract approach, cleaned data from which single paper duplicate values have been removed, and standardized data where all materials that were uniquely identifiable are in a standard form of A X B Y C Z … (where A,B,C,… are elements and X,Y,Z,… are mole fractions). The standardization required post-processing which we accomplished manually and with a combination of further prompting with an LLM, text processing with regular expression and pymatgen 39 . While this standardization approach may introduce some additional errors, it provides a very useful form for the data with modest amounts of human effort. While we were able to employ further prompting combined with LLMs and text analysis tools to make this conversion, the approaches necessitate substantial additional prompt engineering and coding, which was time consuming and likely not widely applicable without significant changes. Given these limitations of our present approach to generating a standardized database from a raw database we do not discuss the details of our approach or attempt to provide a guide on how to do this most effectively. Automating the development of a standardized database from a raw database is an important topic for future work.

To limit the scope of critical cooling rates to just metallic glasses, and yield strengths to high entropy alloys, we first limited the source papers by providing a specified query to the publisher’s database to return only papers relevant to the family of systems we were after, and then we applied a simple composition-based filter to the final standardized database. Details about both these steps are given in the following section when discussing the respective databases.

The first database is of critical cooling rates in the context of metallic glasses. In addition, the critical cooling rate database serves as a larger scale, real-life case assessment of ChatExtract, complementing the test bulk modulus dataset in evaluating the method’s effectiveness. A fully manually extracted dataset of critical cooling rates was prepared to serve as ground truth to compare to the data extracted with ChatExtract. The details of the comparison and evaluation are given later in this section. The critical cooling rate dataset has been chosen due to several aspects that also make it a representative and demanding example. The critical cooling rate is often determined as a result of experimenting with different cooling rates (not critical cooling rates), which are values of similar magnitude and with the same units as critical cooling rates, making them very easy to confuse. In addition, critical cooling rates are sometimes described as a cooling rate for vitrification, or a cooling rate for amorphization, making extraction of the proper value very challenging. Critical cooling rate is also often present alongside other critical values, such as critical casting diameters. The units of critical cooling rates are also not straightforward as they often contain the degree symbol, while the unit of time in the denominator is often expressed in the form of an exponent.

To obtain source research articles we performed a search query “bulk metallic glass” + “critical cooling rate” from Elsevier’s ScienceDirect database which returned 684 papers, consisting of 110126 sentences.

A reference database (ground truth), which we will call R c 1 , was developed using a thorough manual data extraction process based on text processing and regular expressions and aided by a previous database of critical cooling rates extracted with a more time consuming and less automated approach that involved significant human involvement 29 . This laborious process done by an experienced researcher although highly impractical, labor-intensive and time consuming, is capable of providing the most complete and accurate reference database, allowing to accurately evaluate the performance of ChatExtract in a real database extraction scenario, which is the most relevant assessment of the method.

To develop the critical cooling rate database with ChatExtract , the ChatExtract approach was applied identically as to the bulk modulus case except that the phrase “bulk modulus” was replaced with “critical cooling rate”. We call this dataset R c 2 . In comparing R c 2 data to R c 1 ground truth, the same rules for equivalency of triplet datapoints have been applied in the same way as the benchmark bulk modulus data (see “Methods”): equivalent triplets had to have identical units and values (including inequality symbols, if present), and material names had to be similar enough to allow entries to be uniquely identified as the same materials system (e.g., “Mg 100− x Cu x Gd 10 (x = 15)” was the same as Mg 85 Cu 15 Gd 10 , but not the same as “Mg-Cu-Gd metallic glass” and “Zr-based bulk metallic glass” was the same as “Zr-based glass forming alloy” but not the same as Zr 41.2 Ti 13.8 Cu 12.5 Ni 10.0 Be 22.5 ). Critical cooling rates for bulk metallic glasses proved to be quite a challenging property to extract. The analyzed papers very often (much more often than in the other properties we worked on) contained values of critical cooling rates described as ranges or limits, and the materials were often families or broad group of materials, in particular in the “Introduction” sections of the papers. The ChatExtract workflow is aimed at extracting triplets of materials, value, and units without specifying further what do these mean exactly, as will be discussed in the next paragraph. To provide the most comprehensive assessment, the human curated database contains all mentions of critical cooling rates that are accompanied by any number, no matter how vague or specific. This manually extracted, very challenging raw database contained 721 entries. ChatExtract applied on the same set of research papers resulted in 634 extracted values with 76.9% precision and 63.1% recall. The vast majority of reduction in precision and recall comes from the more ambiguous material names such as the above mentioned broad groups or families of materials or ranges and limits of values. In many cases the error in extraction was minor, such as a missing inequality sign (e.g., “<0.1” in R c 1 but “0.1” in R c 2 ), extracting only one value from a range (e.g., “10–100” in R c 1 but only “10” in R c 2 ), or missing details in materials described as a group or family (e.g., “Zr-based metallic glasses” in R c 1 but only “Metallic glasses” R c 2 ). Even though these could be regarded as minor errors, we still consider such triplets to be incorrect. The performance is slightly improved for the cleaned database where a precision of 78.1% and 64.3% recall is obtained with 637 and 553 entries in R c 1 and R c 2 , respectively. The most relevant standardized version of the database, when extracted with ChatExtract yielded a final precision of 91.9% and 84.2% recall, with 313 and 286 entries subject for comparison in R c 1 and R c 2 , respectively. This large reduction in the size of the standardized database when compared to cleaned , and the improvement in performance, are both due to the large amount of material groups/families and ranges/limits of values. These cases do not classify as uniquely identifiable material compositions and discrete values so they do not satisfy the requirements for the standardized database, and as mentioned before, they were the most problematic for ChatExtract to extract (as they were for the human curating R c 1 ). It is important to note that in order to provide an accurate assessment of the extraction performance, as mentioned previously, the triplets are not matched by themselves, but they also have to originate from the same text passage. Therefore both the ground truth and the ChatExtract extracted databases were standardized separately, and if either contained a standardized value, it was considered in the assessment, making the comparison more challenging. The performance of ChatExtract for the standardized database of critical cooling rates is close to that for bulk modulus presented in Table  1 and demonstrates the transferabilty of ChatExtract to different properties.

In addition, we extracted 348 raw datapoints from tables, some which were duplicates of values already extracted from text data, adding only 277 new points to the standardized database and consisting of 97 new unique compositions. We also positively classified 208 figures as relevant and provided their source document and caption, but data from figures has not been manually extracted.

The final standardized database obtained with ChatExtract consists of 557 datapoints. Duplicate values originating from within a single paper have already been removed for the cleaned database, but duplicate triplets originating from different papers are still present. We believe it is important to keep all values, as it allows for an accurate representation of the frequency at which different systems are studied and for accurate averaging if necessary. If the duplicates were to be removed, 309 unique triplets would be left, with the many duplicates being for an industry standard system Zr 41.2 Ti 13.9 Cu 12.5 Ni 10 Be 22.5 (Vit1). The values in the final database ranged from 10 −3 Ks −1 (for Ni 40 P 20 Zr 40 ) to 4.619  ⋅  10 13 Ks −1 (for CuZr 2 ), with an average around 10 2 Ks −1 , all quite reasonable values. An additional standardized-MG database is given, in which all non-metallic materials have been removed. In the case of this modest-sized database, simply removing oxygen containing systems proved to be enough and 5 non-metallic oxide materials have been removed. Out of the 309 unique datapoints, there were 222 unique material compositions (some compositions had multiple values originating from different research papers) in the standardized database, and after removing non-metallic systems standardized-MG database contained 298 unique datapoints for 217 unique material compositions. This size of 217 unique compositions is significantly larger than the previous largest hand-curated database published by Afflerbach et al. 40 , which had just 77 entries. This result shows that, at least in this case, ChatExtract can generate more quality data with much less time than human extraction efforts. To further demonstrate the robustness of ChatExtract and compare with other methods, we applied CDE2 on the critical cooling dataset as well. CDE2 performance on the critical cooling rate dataset was consistent with the previous assessment on the bulk modulus dataset, with overall precision and recall of 49.2% and 35.1% respectively. The details on the usage of CDE2 can be found in the “Methods” section.

Finally, we developed a database of yield strength of high entropy alloys (HEAs) using the ChatExtract approach. This database does not have any readily available ground truth for validation but represents a very different property and alloy set than either bulk modulus or critical cooling rate and therefore further demonstrates the efficacy of the ChatExtract approach. In the first step we searched for a combination of the phrase “yield strength” and (“high entropy alloys” or “compositionally complex alloys” or “multi-principle component alloys”) in the Elsevier’s ScienceDirect API. The search returned 4029 research papers consisting of 840431 sentences. 10269 raw data points were extracted. The cleaned database consisted of 8900 datapoints. Further post-processing yielded 4275 datapoints that constitute the standardized data, where we assumed that all compositions were given as atomic %, unless otherwise stated in the analyzed text (which was infrequent). The 4275 standardized datapoints contained a number of alloys that were not were not HEAs, with HEA defined as a systems containing 5 or more elements. The inclusion of non-HEA systems is not an error in ChatExtract as the data was generally in the papers, despite their being extracted by the above initial keyword search. By restricting the database to only HEAs we obtained a final standardized-HEA database of 2442 values. The standardized-HEA database had 636 materials with unique compositions. The values ranged from 12 MPa for Al 0.4 Co 1 Cu 0.6 Ni 1 Si 0.2 to 19.16 GPa for Fe 7 Cr 31 Ni 23 Co 34 Mn 5 . These values are extreme but not unphysical and we have confirmed that both these extremes are extracted correctly. The distribution of yield stress values resembles a positively skewed normal distribution with a maximum around 400 MPa, which is a physically reasonable distribution shape with a peak at a typical yield stress for strong metal alloys. Additionally 2456 raw datapoints were extracted from tables, many of which were duplicates of values already extracted from text data, adding only 195 new unique HEA compositions. We positively classified 1848 figures as relevant and provide their source document and caption, but data from figures has not been manually extracted.

A large automatically extracted database of general yield strengths, not specific to HEAs, has been developed previously 5 . Direct quantitative comparison is not straightforward, but the histogram of values obtained from the previous database exhibits a very similar shape to the data obtained here, further supporting that our data is reasonable. The database of yield strengths for HEAs developed here is significantly larger than databases developed for HEAs previously, for example, databases containing yield strengths for 169 unique HEA compositions from 2018 41 and containing yield strength for 419 unique HEA compositions from 2020 42 . The ChatExtract generated databases are available in Figshare 43 (see “Data availability”).

Now that we developed and analyzed these databases, it is easier to understand the utility of ChatExtract . ChatExtract was developed to be general and transferable, therefore it tackles a fundamental type of data extraction—a triplet of Material, Value, Unit for a specified property, without imposing any other restrictions. The lack of specificity when extracting “Material” or “Value” allows for extraction of data from texts where the materials are presented both as exact chemical compositions, or broad groups or families of systems. Similarly, values may be discrete numbers, or ranges or limits. However, certain restrictions are often desired in developing a database, and we believe that these fall into two broad categories with respect to the challenges of integrating them into the present ChatExtract workflow. The first category is restrictions based on the extracted data, for example, targeting only desired compositions or ranges of a property value. Such restrictions are trivial to integrate with ChatExtract by either limiting the initial search query in the publisher’s database, limiting the final standardized database, or both, based on the restriction. For example, in our HEA database we assured only HEAs in final data by both limiting the search query in the publisher’s database and applying a composition-based rule on the final standardized database. The second category is where we want a property value when some other property conditions hold, for example, the initial property should be considered at a certain temperature and pressure. This situation is formally straightforward for ChatExtract as it can be captured by generalizing the problem from finding the triplet: material, property, unit , to finding the multiplet: material , property 1 , unit 1 , property 2 , unit 2 , …. The ChatExtract workflow can then be generalized to apply to these multiplets by adding more steps to both the left and right branches in Fig.  2 , for example if a temperature at which the data was obtained was relevant, the left branch would contain two more boxes, the first being: Give the number only without units, do not use a full sentence. If the value is not present type “ None ” . What is the value of the temperature at which the value of [property] is given in the following text? , followed by a second similar one prompting for the unit. The first prompt in the right branch would ask for a table that also included a temperature value and temperature unit, followed by two validation prompts for those two columns. This approach could be expanded into extracting non-numerical data as well, such as sample crystalinity or processing conditions. While these generalizations are formally straightforward we have made no assessment of their accuracy in this work, and some changes to ChatExtract might be needed to implement them effectively. For example, any additional constraints or information would have to be included in the text being examined by the LLM, and the more information that is required, the less likely it is that it will all be contained in the examined text passage. Thus the examined text passage may need to be expanded, or sometimes the required additional data may be missing from the paper altogether.

Conclusions

This paper demonstrates that conversational LLMs such as ChatGPT , with proper prompt engineering and a series of follow-up questions, such as the ChatExtract approach presented here, are capable of providing high quality materials data extracted from research texts with no additional fine-tuning, extensive code development or deep knowledge about the property for which the data is extracted. We present such a series of well-engineered prompts and follow-up questions in this paper and demonstrate its effectiveness resulting in a best performance of over 90% precision at 87.7% recall on our test set of bulk modulus data, and 91.6% precision and 83.6% recall on a full database of critical cooling rates. We show that the success of the ChatExtract method lies in asking follow-up questions with purposeful redundancy and introduction of uncertainty and information retention within the conversation by comparing to results when these aspects are removed. We further develop two databases using ChatExtract —a database of critical cooling rates for metallic glasses and yield strengths for high entropy alloys. The first one was modest-sized and served as a benchmark for full database development since we were able to compare it to data we extracted manually. The second one was a large database, to our knowledge the largest database of yield strength of high entropy alloys to date. The high quality of the extracted data and the simplicity of the approach suggests that approaches similar to ChatExtract offer an opportunity to replace previous, more labor intensive, methods. Since ChatExtract is largely independent of the used model, it is also likely improve by simply applying it to newer and more capable LLMs as they are developed in the future.

The main statistical quantities used to assess performance of ChatExtract were precision and recall, defined as:

In our assessment we defined true positives (for precision) and false negatives (for recall) in terms of each input text passage, which we define above to consist of a target sentence, its preceding sentence, and the title. The exact approach can be confusing so we describe it concretely for every case. For a given input text passage there are zero, one or multiple unique datapoint triplets of material, value, and unit . We take the hand extracted triplets as the ground truth. We then process the text passage with ChatExtract to get a set of zero or more extracted triplets. If the ground truth has zero triplets and the extracted data has zero triplets, this is a true negative. Every extracted triplet from a passage with zero ground truth triplets is counted as a false positive. If the ground truth has one triplet and the extracted data has zero triplets this is counted as a false negative. If the ground truth has one triplet and the extracted data has one equivalent triplet (we will define “equivalent” below) then this is counted as a single true positive. If the ground truth has one triplet and the extracted data has one inequivalent triplet then this is counted as a single false positive. If the ground truth has one triplet and the extracted data has multiple triplets they are each compared against the ground truth sequentially, assigning them as a single true positive if they are equivalent to the ground truth triplet and a single false positive if they are not equivalent to the ground truth triplet. However, only one match (a match is an equivalent pair of triplets) can be made of an extracted triplet to each ground truth triplet for a given sentence, i.e., we consider the ground truth triplet to be used up after one match. Therefore, any further extracted triplets that are equivalent to the ground truth triplet are still counted as each contributing a single false positive. Finally, we consider the case where ground truth has multiple triplets. In this case, if the extracted data has no triplets it is counted as a multiple false negatives. If the extracted data has one triplet and it is equivalent to any ground truth triplet that is counted as one true positive. If the extracted data has multiple triplets each one is compared to each ground truth triplet. If a given extracted triplet is equivalent to any one of the ground truth triplets that extracted triplet is counted as a true positive. However, as above, each ground truth triplet can only be matched once, and any addition matches of extracted triplets to an already matched ground truth triplet are counted as one additional false positive.

In the above we defined “equivalent” triplets in the following way. First, equivalent triplets had to have identical units and values (if uncertainty was present, it did not have to be extracted, but if it was extracted it had to be extracted properly as well). Second, equivalent triplets had to have materials names in the ground truth and extracted text that uniquely identified the same materials system (e.g., Li 17 Si (4− x ) Ge x (x = 2.3) and Li 17 Si 1.7 Ge 2.3 would be equivalent but “Zr-Ni alloy” and “Zr 62 Ni 38 ” would not). These requirements for equivalent triplets are quite unforgiving. In particular, in many cases where we identified false positives the LLM extracted data that was partially right or had just small errors. This fact suggests that better precision and recall might be obtained with some human input or further processing. Overall we believe the above methods provide a rigorous and demanding assessment of the ChatExtract approach.

OpenAI ChatGPT API was used within Python 3.10.6. To maximize reproducibility and consistency in responses we specifically used the gpt-3.5-turbo-0301 snapshot model of GPT-3.5, and gpt-4-0314 snapshot model of GPT-4, in both of which the model parameters were set as follows: temperature = 0.0, frequency_penalty = 0.0, presence_penalty = 0.0, top_p = 1.0, logprobs = False, n  = 1, logit_bias and stop has been set to the default null . For the LLaMA2-chat 70B model, temperature = 0.0, and top_p = 1.0 were used, with a batch size of 6. No system prompts (empty strings) were used in any of the models. When using ChemDataExtractor2 for extracting critical cooling rates, the specifier expression was prepared based on how we found the critical cooling rate is addressed in our test data: “critical cooling rate”, “Rc”, “R c”, “R_c”, “RC”, “R_C”, “R C”, “R c”, “CCR”. A new unit type for units of cooling rate was prepared, which included all units we encountered in our test data: “C/s”, “K/min”, “K/s”, “K^(−1)”, “Kmin^(−1)”, “Ks-1”, “Ks^(−1)”, “°C/min”, “°C/s”, “°Cs^(−1)”.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The extracted databases of critical cooling rates of metallic glasses and yield strength for HEAs, as well as data used in the assessment of the models is available on figshare 43 : https://doi.org/10.6084/m9.figshare.22213747 . In the case of data related to figures, we do not provide the figure file due to copyrights, but instead provide the figure numbers, figure captions, and and the DOI of the source document, which allows for an easy and precise identification of the figures. In that repository, we also provide a version of the python code we used for data extraction that follows the workflow presented in Fig.  2 , and involves additional simple post-processing of the ChatGPT responses to follow the workflow and provide a more convenient output. The post-processing included in the example code is relatively simple, and while it worked well for the properties we studied here, there may be cases when it fails in processing responses for different datasets, since occasionally ChatGPT may format its response in an unexpected way. The provided code is just a simple example of how ChatExtract could be implemented and has limited error handling.

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Acknowledgements

D.M. and M.P.P. acknowledge the support from National Science Foundation Cyberinfrastructure for Sustained Scientific Innovation (CSSI) Award No. 1931298. This work used Bridges-2 44 at Pittsburgh Supercomputing Center through allocation MCA09X001 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

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M. P. P. conceived the study, performed the modeling, tests and prepared/analyzed the results, D. M. guided and supervised the research. Writing of the manuscript was done by M. P. P. and D. M.

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Polak, M.P., Morgan, D. Extracting accurate materials data from research papers with conversational language models and prompt engineering. Nat Commun 15 , 1569 (2024). https://doi.org/10.1038/s41467-024-45914-8

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  8. PDF ON THE NATURE AND NURTURE OF LANGUAGE

    Please address all correspondence to Elizabeth Bates, Center for Research in Language 0526, University of California at San Diego, La Jolla, CA 92093-0526, or [email protected]. 2. ON THE NATURE AND NURTURE OF LANGUAGE Elizabeth Bates. Language is the crowning achievement of the human species, and it is something that all normal humans can do.

  9. Nature Of Language Essay

    Nature Of Language Essay 1861 Words8 Pages Language is a communication system that includes sounds, letters, and grammar. Also, it is a communication system, used by people in a country or in a particular work environment.

  10. The Nature and Origin of Language

    The Nature and Origin of Language Get access Denis Bouchard Published: 26 September 2013 Cite Permissions Share Abstract The most important is that it provides a good test for linguistic theories. It considers some current scenarios of the emergence of language.

  11. (PDF) The Nature and Function of Languages

    According to Daniel Dor, language represents the most important technological invention of human beings. Seemingly, the main function of language consists of its ability to allow the sharing of ...

  12. Essay On Nature Of Human Language

    However, the origin of human language still mystery until now. According to Yule (2006 p.1), spoken language develop first than written language around 100,000 - 50,000 years ago but the evidences and artifacts about how language back in early stage are never found until now. The nature about human language is it comes naturally in our life.

  13. (PDF) The Nature of Human Language and Its Characteristics from a

    In this context, this paper aims to explore the nature of human language and its key characteristics from a semiotic perspective using a real-life scenario where we explain how a message is conveyed through signals and channels. Additionally, we examine the nature of human language, referring to the definition Bloch and Trager gave in 1942.

  14. The Natures Of The Language And The Nature Of Language

    The Natures Of The Language And The Nature Of Language 1754 Words 8 Pages Cambridge Dictionary states that language is "a system of communication consisting of sounds, words, and grammar, or the system of communication used by people in a particular country or type of work".

  15. On Nature and Language

    The volume concludes with an essay on the role of intellectuals in society and government. Nature and Language is a significant landmark in the development of linguistic theory. It will be welcomed by students and researchers in theoretical linguistics, neurolinguistics, cognitive science and politics, as well as anyone interested in the ...

  16. Language as shaped by the environment: linguistic construal in ...

    Different languages carve up the world in quite different ways. Notable examples include the way languages divide the same continuous colour space in different numbers of basic colour terms ...

  17. Essay on the Nature and Uses of Language

    Nature of Language 1) What is language? Language is a collection of symbols governed by rules and used to convey messages between individuals. The nature of language brings us to the nature of human thought and action, for language is neither more nor less than both these aspects of human nature. Let's examine the nature of language closer.

  18. The Nature of Language, Essays, Essays for Children, School Essays

    The Nature of Language To what extent does the nature of language illuminate our understanding of the relation between knowledge of ourselves and knowledge of others? More than any other thing, the use of language sets humankind apart from the remainder of the animal kingdom.

  19. Essay on Language and Communication

    Nature of Language: Language is essentially a set of signs and symbols which have certain fixed meanings, evolved in each society. It is this fixed denotation which makes languages so essential and crucial. The signs or symbols may be sounds, words, light signals, gestures, facial expressions, geometrical signs, and body postures.

  20. (PDF) An essay on the different views of language

    Language is a system of speech sounds used for human communication. It a psychological tool used to solve problems. It is the expression of ideas by means of speech-sounds combined into words....

  21. Learning the language of nature

    Nature has brought forth intelligences, minds, and languages. We need to come to our senses and begin to listen. Bridle offers this example: The eye evolved independently, twice - once in the class of cephalopods, and a second time, in vertebrates. He suggests that the mind, too, has evolved more than once independently, in different species.

  22. Nature Essay for Students and Children

    500+ Words Nature Essay Nature is an important and integral part of mankind. It is one of the greatest blessings for human life; however, nowadays humans fail to recognize it as one. Nature has been an inspiration for numerous poets, writers, artists and more of yesteryears.

  23. Nature Of Language Essay Sample 2023

    Language has several natures, and that is the topic of this essay. Language can either be in a written or verbal form. Written language uses letters, numbers, or imagery symbols which is used by the writers to help the readers identify and understand the message they wish to convey.

  24. 2024 LIFE

    Projects that support the achievement of Union nature and biodiversity objectives by implementing coherent programmes of action in Member States in order to mainstream those objectives and priorities into other policies and financing instruments, including through coordinated implementation of the prioritised action frameworks adopted pursuant to Directive 92/43/EEC.

  25. Extracting accurate materials data from research papers with ...

    There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large ...