Topic
Talking animal
About: Talking animal is a research topic. Over the lifetime, 6 publications have been published within this topic receiving 1237 citations. The topic is also known as: speaking animal.
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TL;DR: The accepted tripartite divisions of the formal study both of mankind's past and present are to a considerable extent based on man's development first of language and later of writing as mentioned in this paper.
Abstract: The accepted tripartite divisions of the formal study both of mankind's past and present are to a considerable extent based on man's development first of language and later of writing. Looked at in the perspective of time, man's biological evolution shades into prehistory when he becomes a language-using animal; add writing, and history proper begins. Looked at in a temporal perspective, man as animal is studied primarily by the zoologist, man as talking animal primarily by the anthropologist, and man as talking and writing animal primarily by the sociologist.
1,185 citations
Book•
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20 Oct 1977
TL;DR: For instance, the authors describes speech as brain-work, speech as ear-work and speech as a guessing game, and speech servo-mechamisms as well as how humans learn to do it.
Abstract: Preface 1. Homo sapinens? 2. Speech as brain-work 3. Speech as tongue-work 4. Speech as sound-waves 5. Speech as ear-work 6. Speech as a guessing game 7. Speech servo-mechamisms 8. How did we learn to do it? 9. Are you right or left brained? 10. When speech goes wrong 11. Thinking, feeling and speaking Index.
30 citations
Book•
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23 Oct 2002
TL;DR: The House of Being is a posthumous publication based on a manuscript originally written by Gordon C. Dickinson in 2016 and then edited by David I. Dickinson.
Abstract: 1. Preface.- 2. The Talking Animal: The Origin of Language.- 3. Getting the Better of Words: Language and Communication.- 4. Virtual Words: Language and Media.- 5. We Are What We Speak: Language and Society.- 6. The Finite Instrument: The Design and Structure of Language.- 7. The Parent of Language: Language and Mind.- 8. Conclusion: The House of Being.- Bibliography.- Index.
11 citations
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TL;DR: Man has been defined in many ways, as the talking animal, the religious animal, and so on, but the most satisfactory definition from the scientific point of view is probably Man the Tool-maker as mentioned in this paper.
Abstract: Man has been defined in many ways, as the talking animal, the religious animal, and so on, but the most satisfactory definition from the scientific point of view is probably Man the Tool-maker. One might ask, why bother to define man, considering his uniqueness and self-evident characteristics? But from the standpoint of students of evolution, particularly those concerned with the interpretation of fossil remains of early man and his possible ancestors, to define man is of practical importance.
11 citations
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01 Jan 2015
TL;DR: This essay addresses the attempt to represent one aspect of lexical semantic linguistic competence (lexical semantic relations) in a major computational resource: WordNet, one of the remarkable discoveries that helped scientists to come closer to the desire to teach machines to speak.
Abstract: A long time passed from the first word said by Homo sapiens to the first word said by a machine. The possibility of machines talking like men confronts us with the richness and complexity of human linguistic competence and its cognitive underpinnings. The contemporary study of linguistics attempts to explain this complexity explicitly. Part of this is lexical semantics, which studies the meanings of words and the relations between them. This essay addresses the attempt to represent one aspect of lexical semantic linguistic competence (lexical semantic relations) in a major computational resource: WordNet. There are various kinds of relations in lexical semantics: homonymy, synonymy, antonymy, hyponymy, meronymy, and troponymy. They were used in WordNet to represent the organisation of the human lexicon. WordNet has a synset as a main building block. Synsets are sets of word forms that are close in meaning in context. In WordNet, nouns and verbs have taxonomic structures. The word forms are divided into domains related to a specific subject and shared features. Adjectives have a structure based on the antonymy relation where bipolar adjectives divided into clusters referring to a certain meaning. Adverbs are gathered in a single file. Psycholinguists have often attacked the WordNet structure as a representation of human linguistic competence. However, computational linguists have found the lexical semantic database useful for machine applications and natural language processing. WordNet have been translated into many languages and combined into multilingual databases such as EuroWordNet. Each language has developed its own wordnet but they are interconnected with interlingual links. Expand and merge approaches are used for data acquisition. The expand approach assumes bilingual translation with automatic, manual and hybrid methods to fill up gaps in data. Linguistic bias between languages can be reduced by data from sources such as Wikipedia or dictionary translation by professional interpreters. The merge approach assumes use of monolingual corpora for data acquisition. WordNet moved from cognitive science to natural language processing. It is one of the remarkable discoveries that helped scientists to come closer to the desire to teach machines to speak.