Skip to main content

Molecules, Meaning and Post-Modernist Semantics

  • Chapter
  • 422 Accesses

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

Wilks’ early English/French Machine Translation system was based on a notion called Preference Semantics. There were two key components of Preference Semantics. First was the notion of combining elementary meaning units of some kind (in Wilks’ case effectively surrogates for Roget thesaurus’ categories) in structures of arbitrary complexity and fineness of description. Second, was the notion of meaning selection: in this case choice of translation term; being one of preferential or balanced ranking rather than absolute selection. While Wilks’ system was driven by a dictionary hand crafted in much the manner of conventional lexicographic work, Wilks’ colleagues (and specifically Spärck Jones) were very interested in what would now be called supervised and unsupervised learning of these lexical structures. Such learning is probably needed to build a practical language processing system based on these ideas. The paper looks at these notions of molecular word meaning definitions and their acquisition in terms of modern developments in supervised and unsupervised learning. It will go on to look further at the notion of preference in the light of post-modernist notions of semantics developed by Zuidervaart amongst others, and then look briefly at how one would go about constructing a Wilks-like Machine Translation system using today’s state of knowledge.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Agirre, E., O.L. de Lacalle and D. Martinez. 2005. Exploring Feature Spaces with SVD and Unlabeled Data for Word Sense Disambiguation. Proceedings of the Conference on Recent Advances in Natural Language Processing (RANLP’05), Borovets, Bulgaria.

    Google Scholar 

  • Allen, J. 1995. Natural Language Understanding. Redwood City, CA: Benjamin Cummings.

    Google Scholar 

  • Argaw, A.A. 2005. Word Sense Discrimination in Query Translation. www. dsv.su.se/∼atelach/Stat/termpaperSTAT.pdf

    Google Scholar 

  • Atkins, B. and B. Levin. 1991. Admitting Impediments. In Lexical Acquisition: Exploiting On-line Resources to Build a Lexicon, ed. U. Zernik, Hillsdale, New Jersey: Lawrence Erlbaum, 233–262.

    Google Scholar 

  • Biber, D. 1993. Co-occurrence Patterns Among Collocations: A Tool for Corpus-Based Lexical Knowledge Acquisition. Computational Linguistics 19(3): 531–538.

    Google Scholar 

  • Boguraev, B. and T. Briscoe (eds.) 1989. Computational Lexicography for Natural Language Processing, Longman.

    Google Scholar 

  • Boguraev, B.K. and K. SpärckJones. 1981 A Natural Language Analyser for Database Access. Information Technology: Research and Development 1: 23–39.

    Google Scholar 

  • Brown, P.F., S.A. Della Pietra, V.J. Della Pietra and R.L. Mercer. 1991. Word-Sense Disambiguation Using Statistical Methods. In Proceedings of the 29th Annual Meeting of the Association for Computational Linguistics (ACL 91), 18–21 June 1991, University of California, Berkeley, California. pp. 264–270.

    Google Scholar 

  • Carter, D. 1987. Interpreting Anaphors in Natural Language Text. Chichester, UK: Ellis Horwood.

    Google Scholar 

  • Chen, H.-C., T. Yim, D. Fye and B. Schatz. 1995. Automatic Thesaurus Generation for an Electronic Community System. Journal of the American Society for Information Science 46(3): 175–193.

    Article  Google Scholar 

  • Church, K.W. and P. Hanks. 1990. Word Association Norms, Mutual Information and Lexicography. Computational Linguistics 16(1): 22–29.

    Google Scholar 

  • Cowie, J., J. Guthrie and L. Guthrie. 1992. Lexical Disambiguation using Simulated Annealing. COLING 92: 359–365, Nantes, France.

    Google Scholar 

  • Daelemans, W, J. Zavrel, P. Berck and S. Gillis. 1996. MBTiA Memory-based POS Tagger generator, Proceedings of the 4th Workshop on Very Large Corpora, Copenhagen, 14–27.

    Google Scholar 

  • Dagan, I., A. Itai and U. Schwall. 1991. Two Languages are More Informative than One. In Proceedings of the 29th Annual meeting of the ACL, 130–137.

    Google Scholar 

  • Dempster, A.P., N.M. Laird and D.B. Rubin. 1977. Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society Series B 39:1–38.

    Google Scholar 

  • Fellbaum, C. 1998. WordNet, an Electronic Lexical Database. Cambridge, London: MIT Press.

    Google Scholar 

  • Fodor, J.A. 1998 Concepts. Oxford University Press.

    Google Scholar 

  • Gale, W., K.W. Church and D. Yarowsky. 1992. A Method for Disambiguating Word Senses in a Large Corpus. Computers and the Humanities 26(5–6): 415–439.

    Article  Google Scholar 

  • Gardner, M. (ed.) 1970. The Annotated Alice-Lewis Carroll. 2nd Edition, Middlesex, UK: Penguin, Harmondsworth.

    Google Scholar 

  • Garside, R., G. Leech and G. Sampson (eds.) 1987. The Computational Analysis of English, a Corpus-Based Approach, London: Longman.

    Google Scholar 

  • Grefenstette, C. 1994. Explorations in Automatic Thesaurus Discovery. Moston, MA: Kluwer Academic Publishers.

    Google Scholar 

  • Hazewinkel, M. 1996. Bipartite Graphs and Automatic Generation of Thesauri, ERCIM News.

    Google Scholar 

  • Hearst, M.A. 1992. Automatic Acquisition of Hyponyms from Large Text Corpora. Proceedings of COLING, 539–545.

    Google Scholar 

  • Hodge, V.J. and J. Austin. 2002. Hierarchical Word Clustering – Automatic Thesaurus Generation. Neurocomputing 48: 819–846.

    Article  Google Scholar 

  • Kennedy, C. and B. Boguraev. 1996. Anaphora for Everyone: Pronominal Anaphora Resolution Without a Parser. Proceedings of the 16th International Conference on Computational Linguistics (COLING ’96), Copenhagen, 113–118.

    Google Scholar 

  • Kilgarriff, A. March 1997. I Don’t Believe in Word Sense. Computers and the Humanities 31(2): 91–113.

    Article  Google Scholar 

  • Kilgarriff, A. and M. Palmer. 2000. Guest Editors of the Special Issue on SENSEVAL. Computers and the Humanities 34: 127–134.

    Article  Google Scholar 

  • Lakoff, G. 1972. Linguistics and Natural logic. In D. Davison and G. Harman (eds.), Semantics and Natural Language, Dordrecht: D. Reidel.

    Google Scholar 

  • Lesk, M. 1986. Automatic Sense Disambiguation Using Machine Readable Dictionaries: How to tell a Pine Cone from an Ice Cream Cone. Proceedings of the 1986 SIGDOC Conference, 24–26.

    Google Scholar 

  • Mihalcea, R. 2004. Co-training and Self-training for Word Sense Disambiguation. Proceedings of the Conference on Natural Language Learning (CoNLL 2004), Boston.

    Google Scholar 

  • Mitkov, R. 2003. Anaphora Resolution. In: The Oxford Handbook of Computational Linguistics, ed. R. Mitkov, Oxford, UK: Oxford University Press.

    Google Scholar 

  • Papineni, K., S. Roukos, T. Ward and W.-J. Zhu. 2001. Bleu: A Method for Automatic Evaluation of Machine Translation. IBM Research Report RC22176(W0109-022) Computer Science.

    Google Scholar 

  • Peat, H. and P. Willett. 1991. The Limitations of Co-ocurrence Data for Query Expansion in Document Retrieval Systems. JASIS, 378–383.

    Google Scholar 

  • Pereira, F., N. Tishby and L. Lee. 1993. Distributional clustering of English words: In Proceedings of the Annual Meeting the Association for Computational Linguistics (ACL 93), Columbus, OH.

    Google Scholar 

  • Pulman, S.G. 2005 “Lexical Decomposition” in Charting a new Course: Natural Language Processing and Information Retrieval – Essays in Honour of Karen Spärck Jones, J.I. Tait (ed.). Springer.

    Google Scholar 

  • Roget, P.M. 1852. Introduction to; Thesaurus of English Words and Phrases.

    Google Scholar 

  • Roussinov, D.G. and H. Chen. 1998. A Scalable Self-organizing Map Algorithm for Textual Classification: A Neural Network Approach to Thesaurus Generation. http://dlist.sir.arizona.edu/460/01/A{%}5FScalable-98.htm Communication, Cognition and Artificial Intelligence, Spring.

    Google Scholar 

  • Ryu, P.-M. and K.-S. Choi. 2005. An Information Theoretic Approach to Taxonomy Extraction for Ontology Learning. In: Buitelaar, P., Cimiano, P. and Magnini, B. (eds.), Ontology Learning from Text: Methods, Evaluation and Applications, Amsterdam: IOS Press, 15–28.

    Google Scholar 

  • Sabou, M., C. Wroe, C. Goble and G. Mishne. Learning Domain Ontologies for Web Service Descriptions: An Experiment in Bioinformatics. Proceedings of the 14th International Conference on the World Wide Web (WWW ’05), Chiba, Japan. May 05, 190–198.

    Google Scholar 

  • Salton, G. 1971. The SMART Retrieval System: Experiments in Automatic Document Processing. New Jersey: Prentice_Hall, Eaglewood Cliffs.

    Google Scholar 

  • Sanderson, M. 2000. Retrieving with Good Sense. Information Retrieval 2(1): 45–65.

    Article  Google Scholar 

  • Scannell, K.P. 2003. Automatic Thesaurus Generation for Minority Languages: An Irish Example. Saint Louis University. TALN 2003, Batz-sur-Mer.

    Google Scholar 

  • Schulte im Walde, S. 2007. The Induction of Verb Frames and Verb Classes from Corpora. To appear as chapter 61 in Lüdeling A. and Kyto, M. (eds.), Corpus Linguistics: An International Handbook. Berlin: Mouton de Gruyter.

    Google Scholar 

  • Schütze, H. and J.O. Pederson. 1995. IR based on word senses, in Fourth Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, NV, 161–175.

    Google Scholar 

  • Schütze, H. and J.O. Pederson. 1997. A Co-occurrence-based Thesaurus and Two Applications to Information Retrieval. Information Processing and Management 33(3): 307–318.

    Article  Google Scholar 

  • Shann. P. Machine Translation: A problem of Linguistic Engineering or of Cognitive Modelling? In: Machine Translation Today: The State of the Art. Proceedings of the 3rd Lugano Tutorial, Lugano, Switzerland, 2–7 April 1984, ed. Margaret King, Edinburgh University Press. 71–90.

    Google Scholar 

  • Spärck Jones, K. 1986 Synonymy and Semantic Classification. Edinburgh University Press.

    Google Scholar 

  • Spärck Jones, K. 2000 RH Richens: Translation in the NUDE. In: W.J. Hutchins (ed.), Early Years in Machine Translation, Amsterdam: John Benjamins, 263–278.

    Google Scholar 

  • Stevenson, M. and Wilks, Y. 2001 Interaction of Knowledge Sources in Word Sense Disambiguation. Computational Linguistics 27(3): 321–349.

    Article  Google Scholar 

  • Sussna, M. 1993 Word Sense Disambiguation for Free-Text Indexing Using a Massive Semantic Network. CIKM ’93, 11/93, DC, USA. Proceedings of the second international conference on information and knowledge management (cikm-93), Arlington Virginia.

    Google Scholar 

  • Tait, J.I. (ed.), 2005 Charting a New Course: Natural Language Processing and Information Retrieval: Essays in Honour of Karen Spärck Jones. Springer.

    Google Scholar 

  • Thomas, J. and A. Wilson. 1996 Methodologies for Studying a Corpus of Doctor-Patient Interaction In: J. Thomas and M. Short (eds.), Using Corpora for Language Research, Harlow: Longman, 92–109.

    Google Scholar 

  • Voorhees, E. 1993 Using WordNet to Disambiguate Word Senses for Text Retrieval. ACM SIGIR ’93 Pittsburgh, PA USA, 171–180.

    Google Scholar 

  • Wilks, Y. Text Searching with Templates. Cambridge Language Research unit Memo ML156. 1964. Reproduced in the companion volume to this book.

    Google Scholar 

  • Wilks, Y. 1971 The Stanford Machine Translation Project. In: R. Rushton (ed.), Natural Language Processing, New York, NY, USA: Algorithmics Press.

    Google Scholar 

  • Wilks, Y. 1973 An Artificial Intelligence Approach to Machine Translation. Chapter 2 R. Schank and K.M. Colby (eds.), Computer Models of Thought and Language. San Francisco: W H Freeman and Co., 114–151.

    Google Scholar 

  • Wilks, Y. An Intelligent Anlayzer and Understander of English. Communications of the ACM 18(55), 1975a. Reproduced in the companion volume to this book.

    Google Scholar 

  • Wilks, Y. A Preferential, Pattern-Seeking, Semantics for Natural Lnguage Inference. Artifical Intelligence 6. 1975b. Reproduced in the companion volume to this book.

    Google Scholar 

  • Wilks, Y. 1975c Seven Thesis on Artificial Intelligence and Natural Language. Working Paper No. 17. Fondazione Dalle Molle per gli studi linguistici e di comunicazione internzionale.

    Google Scholar 

  • Wilks, Y. 1976 Parsing English II. In: Charniak, E. and Wilks, Y. (eds.), Computational Semantics, North-Holland, Amsterdam, 155–185.

    Google Scholar 

  • Wilks, Y.A. Frames, Semantics and Novelty.s 1980 In Metzing (ed.), Parsing Natural Language, 219–46.

    Google Scholar 

  • Wilks, Y., D. Fass, C.-M. Guo, J. McDonald, T. 1989 Plate and B. Slator. A tractable machine dictionary as a resource for computational semantic. In: Boguraev and Briscoe (eds.).

    Google Scholar 

  • Wilks, Y.A., B.M. Slator and L.M. Guthrie. 1996 Electric Words: Dictionaries, Computers and Meanings. Cambridge, MA, USA: MIT Oress.

    Google Scholar 

  • Wilks, Y. Senses and Texts. Computers and the Humanities 31:77–90, 1997. Reproduced in the companion volume to this book.

    Google Scholar 

  • Wilks, Y. and M. Stevenson. 1996 The Grammar of Sense: Is Word-Sense Tagging Much More than Part-of-Speech Tagging? Technical Report CS-96-05, University of Sheffield.

    Google Scholar 

  • Wilks, Y.A. and J.I. Tait. A Retrospective View of Synonymy and Semantic Classification. In Tait (2005) 1–11.

    Google Scholar 

  • Winograd, T. and F. Flores. 1986 Understanding Computers and Cognition: A new Foundation for Design. Norwood, NJ, USA: Ablex Publishing.

    Google Scholar 

  • Yarowsky, D. 1992 Word Sense Disambiguation Using Statistical Models of Roget’s Categories Trained on Large Corpora. Proceedings of COLING, Nantes, France, 454–460.

    Google Scholar 

  • Yarowsky, D. 1995 Unsupervised Word Sense Disambiguation Rivalling Supervised Methods. Proceedings of 33rd ACL (ACL-95) Cambridge, MA, 189–196.

    Google Scholar 

  • Zuidervaart, L. 2004 Artistic Truth: Aesthetics, Discourse and Imaginative Disclosure, Cambridge University Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer

About this chapter

Cite this chapter

Tait, J., Oakes, M. (2007). Molecules, Meaning and Post-Modernist Semantics. In: Ahmad, K., Brewster, C., Stevenson, M. (eds) Words and Intelligence II. Text, Speech and Language Technology, vol 36. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5833-0_13

Download citation

Publish with us

Policies and ethics