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Opening the World with Active Words and Concept Triggers

  • Evelyne Viegas
Part of the Text, Speech and Language Technology book series (TLTB, volume 10)

Abstract

In this paper we present a proposal to help bypass the bottleneck of knowledge-based NLP systems having to work under a closed world assumption. We propose ways of reinterpreting static sources as active ones, by allowing a system to create new lexical entries on the fly, and investigate how to create new concepts on the fly. We argue that a computational lexical semantic approach is a sine qua non to work under an open world assumption. More specifically, we show how to create new lexicon entries using lexico-semantic rules and investigate how to create new concepts for unknown words, building a new model to trigger concepts in context.

Keywords

Machine Translation Lexical Entry Active Word Unknown Word Closed World Assumption 
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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References

  1. Briscoe, T., A. Copestake and A. Lascarides. 1995. Blocking. In P. Saint-Dizier and E, Viegas (eds.) Computational Lexical Semantics. Cambridge, UK: Cambridge University Press.Google Scholar
  2. Grize, B. 1990. Logique et langage. Paris: Ophrys.Google Scholar
  3. Hearst, G. 1992. Automatic acquisition of hyponyms from large text corpora. In the Proceedings of the International Conference on Computational Linguistics,Volt. Jackendoff, R. S. 1983. Semantics and Cognition. Cambridge, MA: The MIT Press.Google Scholar
  4. Mahesh, K. and S. Nirenburg. 1995. A situated ontology for practical NLP. In the Proceedings of the Workshop on Basic Ontological Issues in Knowledge Sharing, IJCAI-95,Montréal, Canada.Google Scholar
  5. Mahesh, K. 1996. Ontology Development: Ideology and Methodology. MCCS-96–292. Memoranda in Computer and Cognitive Science. New Mexico State University: Computing Research Laboratory.Google Scholar
  6. Mahesh, K., S. Nirenburg and S. Beale. 1997. If you have it Flaunt it. In the Proceedings of the TM197,Santa Fe.Google Scholar
  7. Meyer, I., B. Onyshkevych and L. Carlson. 1990. Lexicographic Principles and Design for knowledge-based Machine Translation. CMU-CMT-90–118. Carnegie Mellon University.Google Scholar
  8. Nirenburg, S., S. Beale, S. Helmreich, K. Mahesh, E. Viegas and R. Zajac. 1996. ‘Two principles and six techniques for rapide MT development. In the Proceedings of the AMTA 96.Google Scholar
  9. Onyshkevysh B. and Nirenburg, S. 1995. A Lexicon for knowledge-based MT Machine Translation 10: 1–2.CrossRefGoogle Scholar
  10. Ostler, N. and S. Atkins. 1992. Predictable meaning shift: Some linguistic properties of lexical implication rules. In J. Pustejovsky and S. Bergler (eds.) Lexical Semantics and Knowledge Representation. Berlin: Springer, pp. 87–100.CrossRefGoogle Scholar
  11. Pustejovsky, J. 1991. The Generative Lexicon. Computational Linguistics, 17 (4), 1991, pp. 409–441.Google Scholar
  12. Pustejovsky, J. 1995. The Generative Lexicon. MIT Press.Google Scholar
  13. Pollard, C. and I. Sag. 1987. An Information-based Approach to Syntax and Semantics: Volume 1 Fundamentals. Stanford, CA: CSLI Lecture Notes 13.Google Scholar
  14. Resnik, P. 1992 A class-based approach to lexical discovery. In the Proceedings of the Association for Computational Linguistics’92.Google Scholar
  15. Schank, R. 1975. Conceptual Information Processing. Amsterdam: North-Holland. Schnattinger, K. and U. Hahn. 1998. Quality-Based Learning. In the Proceedings of the 13th European Conference on Artificial Intelligence.Google Scholar
  16. Sowa, J. 1984. Conceptual Structures: information processing in mind and machine. MA: Addison Wesley.Google Scholar
  17. Viegas, E. 1993. La lexicalisation dans sa relation avec la conceptualisation: problèmes théoriques. Doctorat Nouveau Régime, Université Toulouse-le-Mirail.Google Scholar
  18. Viegas, E., B. Onyshkevych, V. Raskin and S. Nirenburg. 1996a. From Submit to Submitted via Submission: on Lexical Rules in Large-scale Lexicon Acquisition. In the Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics,Santa-Cruz, CA.Google Scholar
  19. Viegas, E., M. Gonzalez and J. Longwell. 1996b. Morpho-semantics and Constructive Derivational Morphology: a Transcategorial Approach to Lexical Rules. Memoranda in Computer and Cognitive Science, MCCS-96–295. New Mexico State University: Computing Research Laboratory.Google Scholar
  20. Viegas, E. and V. Raskin. 1998. Computational Semantic Lexicon Acquisition: Methodology and Guidelines. Memoranda in Computer and Cognitive Science, MCCS-98–315, New Mexico State University: Computing Research Laboratory.Google Scholar
  21. Wermter, S., E. Riloff and G. Scheler. 1996. Learning Approaches for Natural Language Processing. In S. Wermter, E. Riloff and G. Scheler (eds.) Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. Springer.Google Scholar
  22. Zernik, U. and P. Jacobs. 1990. Tagging for Learning: collecting thematic relations from corpus. In the Proceedings of the International Conference on Computational Linguistics ‘80,Voll.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • Evelyne Viegas
    • 1
  1. 1.Computing Research LaboratoryNew Mexico State UniversityLas CrucesUSA

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