Three Steps in Wilks Work: From Theory to Resources to Practice

Part of the Text, Speech and Language Technology book series (TLTB, volume 36)

Some researchers are brilliant, able to couch their work in a theory, tracing a line through a long tradition of thinkers, culminating with the advances that they themselves make to body of knowledge that we call science. Some others are head-down, hard workers, butting up against problems and wearing them down over time, hacking out a path through the unknown, a path that others may soon tread over. A very few rare scientists possess both qualities, an enormous capacity for work propelling an unflagging forward movement, and a wide scientific and philosophic culture that they can use to situate what they are doing and to explain why they are doing it. Yorick Wilks is such a scientist whose body of work demonstrates both aspects of hard working obstinacy and cultured brilliance


Natural Language Processing Information Extraction Dictionary Entry Word Sense Computational Linguistics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Amsler, R.A. (1980) The Structure of the Merriam-Webster Pocket Dictionary. PhD Thesis, University of Texas.Google Scholar
  2. Cunningham, H., R.J. Gaizauskas and Y. Wilks. (1995) A General Architecture for Text Engineering (GATE) – a new approach to Language Engineering R&D. Technical Report CS – 95–21, Computer Science, University of SheffieldGoogle Scholar
  3. Fillmore, C. (1968) The case for case. In: E. Bach and R. Harms (eds.) Universals in Linguistics theory. New York: Holt, Rinehart, and Winston.Google Scholar
  4. Gaizauskas, R. and Y. Wilks. (1998) Information Extraction: Beyond Document Retrieval. Journal of Documentation, 54(1):70–105.CrossRefGoogle Scholar
  5. Katz, J.J. and J.A. Fodor. (1963) The Structure of Semantic Theory. Language, 39:170–210.CrossRefGoogle Scholar
  6. Kittredge, R. (1982) Variation and homogeneity of sublanguages. In: R. Kittredge and J. Lehrberger (eds.) Sublanguage: Studies of Language in Restricted Semantic Domains. Berlin: de Gruyter, pp. 107–137.Google Scholar
  7. Lindsay, R., Buchanan, B., Feigenbaum, E. and Lederberg, J. (1993) DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation. Artificial Intelligence, 61(2):209–261.CrossRefGoogle Scholar
  8. Michiels, A. (1982) Exploiting a Large Dictionary Data Base. PhD Thesis, Université de Liége, Liége, Belgium.Google Scholar
  9. Minsky, M. (1975) A framework for representing knowledge. In: P.H. Winston (ed.) The Psychology of Computer Vision. New York: McGraw-Hill, pp. 211–277.Google Scholar
  10. Schank, R.C. (1975) Conceptual Information Processing. Amsterdam: North-Holland Publishing Company.Google Scholar
  11. Shortliffe, E. (1976) MYCIN: Computer-based Medical Consultations. New York: American Elsevier.Google Scholar
  12. Sparck Jones, K. (2000) R.H. Richens: translation in the NUDE. In: W.J. Hutchins (ed.) Early Years in Machine Translation., Amsterdam: John Benjamins, pp. 263–278Google Scholar
  13. Wilks, Y. (1971) Logic, Linguistics and Computational Linguistics. In the Proceedings of the International Conference on Computational Linguistics, Debrecen, Hungary.Google Scholar
  14. Wilks, Y. (1975) An intelligent analyzer and understander of English. Communications of the ACM, 18(5):264–274.CrossRefGoogle Scholar
  15. Wilks, Y. (1976) Frames, Scripts, Stories, and Fantasies. In the Proceedings of the International Conference on the Psychology of Language, Stirling, 1976, and in Pragmatics Microfiche 1977. Reprinted in H. Stegentritt (ed.) Regenburg Romanistentag. Berlin: De Gruyter.Google Scholar
  16. Wilks, Y. (1977) Knowledge Structures and Language Boundaries. In the Proceedings Fifth International Conference on Artificial Intelligence. MIT Press.Google Scholar
  17. Wilks, Y. (1978) Making Preferences More Active, Artificial Intelligence. Vol. 11, pp. 197–223.CrossRefGoogle Scholar
  18. Wilks, Y. (1984) Is Frege’s Principle Trivial or False? In the Proceedings of the Annual Conference of the Linguistics Association of G.B. Essex University.Google Scholar
  19. Wilks, Y. (1985) Relevance, Points of View and Speech Acts: An Artificial Intelligence View. In the Proceedings of the Cognitive Science Conference. Paris.Google Scholar
  20. Wilks, Y. (1987) On Keeping Logic in its place. In the Proceedings of the Third International Workshop on Theoretical Issues in Natural Language Processing (Tinlap3). Las Cruces, New Mexico.Google Scholar
  21. Wilks, Y. and Ballim, A. (1987) The Heuristic Ascription of Belief. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’87), Milan. A fuller version In Review of Cognitive Science, Vol I. N. Sharkey (ed.), London: Ablex, 1989Google Scholar
  22. Wilks, Y., Fuss, D., Gou, C.M., McDonald, J.E., Plate, T. and Slator, B.M. (1990) Providing Machine Tractable Dictionary Tools, Machine Translation 5(2):99–154CrossRefGoogle Scholar
  23. Winograd, T. (1972) Understanding Natural Language. New York: Academic Press.Google Scholar
  24. Winograd, T. and F. Flores. (1986) Understanding Computers and Cognition. Norwood, NJ, USA: Ablex Publishing Corporation.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  1. 1.CEA LISTFontenay aux RosesFrance

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