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10 - Language and communication

Published online by Cambridge University Press:  05 July 2014

Yorick Wilks
Affiliation:
University of Sheffield
Keith Frankish
Affiliation:
The Open University, Milton Keynes
William M. Ramsey
Affiliation:
University of Nevada, Las Vegas
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Summary

Introduction

Language and communication, considered as relevant to artificial intelligence (AI) in general, I take to refer to the issues that cluster round the representation of language and meaning so as to enable language processing and the communication of meaning by a computer, an area of research roughly captured by the fields of Natural Language Processing (NLP) and Computational Linguistics (CL).

A remarkable feature of the fifty-year history of those two related fields is how much of what we now take as topics of current interest was there from the very beginning; all the pioneers lacked were real computers. In the fifties and sixties, Gilbert King was arguing for machine translation by statistical methods, which is only now a reality, Margaret Masterman for the power of meaning-based structures in programs, and Vic Yngve, still working at the time of writing, had designed COMIT, a special programming language for NLP, and had stated his famous claim that limits on the way computers process language should reflect the way the syntax of a language is structured. This last project brought Yngve into direct conflict with Noam Chomsky over permissible ways of drawing syntactic tree structures, which can now be seen to have constituted a defining moment of schism in the history of NLP in its relationship to mainstream linguistics. Chomsky has always denied any relevance of computation to the understanding of language structure, and this foundational schism was not healed until decades later, when Gerald Gazdar became the first major linguist to embrace a computational strategy explicitly.

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Publisher: Cambridge University Press
Print publication year: 2014

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References

Allen, J. (1995). Natural Language Understanding (2nd edn.). Redwood City, CA: Benjamin/Cummings. The best survey of NLP/CL work from an AI perspective by a major theoretical contributor.Google Scholar
Charniak, E. (1993). Statistical Language Learning. Cambridge, MA: MIT Press. Short, accessible introduction to the motivations and methods of the statistical movement in NLP/CL.Google Scholar
Gazdar, G. and Mellish, C. (1989). Natural Language Processing in PROLOG and Natural Language Processing in LISP. Reading, MA: Addison-Wesley. A classic programming language and algorithmic approach to symbolic CL/NLP.Google Scholar
Grosz, B. J., Spärck Jones, K., and Webber, B. L. (1986). Readings in Natural Language Processing. Los Altos, CA: Morgan Kaufmann. This reader contains many classic papers from the first decades of CL/NLP.Google Scholar
Jurafsky, D. and Martin, J. H. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2nd edn.). Upper Saddle River, NJ: Prentice Hall. An excellent survey that also covers the links between speech and language processing in NLP.Google Scholar
Pinker, S. (1997). How the Mind Works. New York: Norton. A statement of the assumptions behind the Chomskyan approach to language modeling, updated beyond Chomsky’s own work to take account of the fact that language structure has itself evolved.Google Scholar
van Deemter, K. (2010). Not Exactly: In Praise of Vagueness. Oxford University Press. A representative volume on the formal semantics approach to NLP, including the possibility of modeling vague concepts.Google Scholar
Wilks, Y. and Brewster, C. (2009). Natural Language Processing as a Foundation of the Semantic Web. Now Press: London. This book contains a great deal of background on IE and on ontology building and maintenance by NLP techniques and their relationship to Semantic Web construction.Google Scholar
Wilks, Y. A., Slator, B. M., and Guthrie, L. M. (1996). Electric Words: Dictionaries, Computers and Meanings. Cambridge, MA: MIT Press. An account of meaning representation in NLP/CL, particularly the use of dictionaries as resources for meaning structures in language processing.Google Scholar
Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The semantic web, Scientific American 284(5): 34–43.CrossRefGoogle Scholar
Brown, P. F., Cocke, J., Della Pietra, S. A., Della Pietra, V. J., Jelinek, F., Lafferty, J. D., Mercer, R. L., and Roossin, P. S. (1990). A statistical approach to machine translation, Computational Linguistics 16: 79–85.Google Scholar
Ciravegna, F., Chapman, S., Dingli, A., and Wilks, Y. (2004). Learning to harvest information for the semantic web, in Bussler, C., Davies, J., Fensel, D., and Studer, R. (eds.), The Semantic Web: Research and Applications: First European Semantic Web Symposium (ESWS04) (pp. 312–26). Berlin: Springer.Google Scholar
Cowie, J. and Wilks, Y. (2000). Information extraction, in Dale, R., Moisl, H., and Somers, H. (eds.), Handbook of Natural Language Processing, (pp. 249–69). New York: Marcel Dekker.Google Scholar
Horrocks, I. (2005). Description logics in ontology applications, in Beckert, B. (ed.), Automated Reasoning with Analytic Tableaux and Related Methods (Lecture Notes in Artificial Intelligence 3702) (pp. 2–13) Berlin: Springer.CrossRefGoogle Scholar
Manning, C. D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press.Google Scholar
McCarthy, J. and Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence, in Meltzer, B. and Michie, D. (eds.), Machine Intelligence 4 (pp. 463–502). Edinburgh University Press.Google Scholar
Nirenburg, S. and Wilks, Y. 2001. What’s in a symbol: Ontology, representation and language, Journal of Experimental and Theoretical Artificial Intelligence 13: 9–23.CrossRefGoogle Scholar
Parkinson, R. C., Colby, K. M., and Faught, W. S. (1977). Conversational language comprehension using integrated pattern-matching and parsing, Artificial Intelligence 9: 111–134.CrossRefGoogle Scholar
Schank, R. C. and Rieger, C. J. (1974). Inference and the computer understanding of natural language, Artificial Intelligence 5: 373–412.CrossRefGoogle Scholar
Spärck Jones, K. (1999). Information retrieval and artificial intelligence, Artificial Intelligence 141: 257–81.CrossRefGoogle Scholar
Spärck Jones, K. (2003) Document retrieval: Shallow data, deep theories; historical reflections, potential directions, in Sebastiani, F. (ed.), Advances in Information Retrieval: 25th European Conference on IR Research, ECIR 2003 (pp. 1–11). Berlin: Springer.Google Scholar
Thomason, R. (2003). Logic and artificial intelligence, in E. N. Zalta (ed.), The Stanford Encyclopedia of Philosophy (Fall 2003 edn.), .
Wilks, Y. and Fass, D. (1992). The preference semantics family, Computers and Mathematics with Applications 23(2–5): 205–21.CrossRefGoogle Scholar
Winograd, T. (1972). Understanding Natural Language. New York: Academic Press.Google Scholar

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