Skip to main content
SpringerLink
Log in
Book cover

International Conference on Inductive Logic Programming

ILP 1996: Inductive Logic Programming pp 1–22Cite as

Inductive logic programming for natural language processing

  • Invited Talk
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1314))

Abstract

This paper reviews our recent work on applying inductive logic programming to the construction of natural language processing systems. We have developed a system, Chill, that learns a parser from a training corpus of parsed sentences by inducing heuristics that control an initial overly-general shift-reduce parser. CHILL learns syntactic parsers as well as ones that translate English database queries directly into executable logical form. The ATIS corpus of airline information queries was used to test the acquisition of syntactic parsers, and CHILL performed competitively with recent statistical methods. English queries to a small database on U.S. geography were used to test the acquisition of a complete natural language interface, and the parser that Chill acquired was more accurate than an existing hand-coded system. The paper also includes a discussion of several issues this work has raised regarding the capabilities and testing of ILP systems as well as a summary of our current research directions.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Abramson, H., & Dahl, V. (1989). Logic Grammars. Springer-Verlag, New York.

    Google Scholar 

  • Allen, J. F. (1995).Natural Language Understanding (2nd Ed.).Benjamin/Cummings, Menlo Park, CA.

    Google Scholar 

  • Anoe, C., & Bennett, S. W. (1995). Evaluating automated and manual acquisition of anaphora resolution strategies. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pp. 122–129 Cambridge, MA.

    Google Scholar 

  • ARPA (Ed.). (1993). Proceedings of the Fifth DARPA Message Understanding Evaluation and Conference, San Mateo, CA. Morgan Kaufman.

    Google Scholar 

  • Bergadano, F., & Gunetti, D. (1993). An interactive system to learn functional logic programs. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1044–1049 Chambery, France.

    Google Scholar 

  • Berwick, B. (1985). The Acquisition of Syntactic Knowledge. MIT Press, Cambridge, MA.

    Google Scholar 

  • Black, E., & et. al. (1991). A procedure for quantitatively comparing the syntactic coverage of English grammars. In Proceedings of the Fourth DARPA Speech ] and Natural Language Workshop, pp. 306–311.

    Google Scholar 

  • Bloom, P. (1994). Overview: Controversies in language acquisition. In Bloom, P. (Ed.), Language Acquisition: Core Readings, pp. 5–48. MIT Press, Cambridge, MA.

    Google Scholar 

  • Borland International (1988). Turbo Prolog 2.0 Reference Guide. Borland International, Scotts Valley, CA.

    Google Scholar 

  • Brill, E. (1993). Automatic grammar induction and parsing free text: A transformation-based approach. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pp. 259–265 Columbus, Ohio.

    Google Scholar 

  • Brill, E. (1995). Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. Computational Linguistics, 21(4),543–565.

    Google Scholar 

  • Brill, E., & Church, K. (Eds.). (1996). Proceedings of the Conference on Empirical Methods in Natural Language Processing. University of Pennsylvania, Philadelphia, PA.

    Google Scholar 

  • Charniak, E. (1993). Statistical Language Learning. MIT Press.

    Google Scholar 

  • Church, K. (1988). A stochastic parts program and noun phrase parser for unrestricted text. In Proceedings of the Second Conference on Applied Natural Language Processing. Association for Computational Linguistics.

    Google Scholar 

  • Church, K., & Mercer, R. L. (1993). Introduction to the special issue on computational linguistics using large corpora. Computational Linguistics, 19(1), 1–24.

    Google Scholar 

  • Church, K., & Patil, R. (1982). Coping with syntactic ambiguity or how to put the block in the box on the table. American Journal of Computational Linguistics, 8(3-4), 139–149.

    Google Scholar 

  • Cohen, W. W. (1990). Learning approximate control rules of high utility. In Proceedings of the Seventh International Conference on Machine Learning, pp. 268–276 Austin, TX.

    Google Scholar 

  • Collins, M. J. (1996). A new statistical parser based on bigram lexical dependencies. In Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, pp. 184–191 Santa Cruz, CA.

    Google Scholar 

  • De Raedt, L. (1992). Interactive Theory Revision: An Inductive Logic Programming Approach. Academic Press, New York, NY.

    Google Scholar 

  • De Raedt, L., & Bruynooghe, M. (1993). A theory of clausal discovery. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1058–1063 Chambery, France.

    Google Scholar 

  • Estlin, T. A., & Mooney, R. J. (1996). Multi-strategy learning of search control for partial-order planning. In Proceedings of the Thirteenth National Conference on Artificial Intelligence Portland, OR.

    Google Scholar 

  • Fillmore, C. J. (1968). The case for case. In Bach, E., & Harms, R. T. (Eds.), Universals in Linguistic Theory. Holt, Reinhart and Winston, New York.

    Google Scholar 

  • Goodman, J. (1996). Parsing algorithms and metrics. In Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, pp. 177–183 Santa Cruz, CA.

    Google Scholar 

  • Huffman, S. B. (1996). Learning information extraction patterns from examples. In Wermter, S., Riloff, E., & Scheler, G. (Eds.), Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, pp. 246–260. Springer, Berlin.

    Google Scholar 

  • Kijsirikul, B., Numao, M., & Shimura, M. (1992). Discrimination-based constructive induction of logic programs. In Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 44–49 San Jose, CA.

    Google Scholar 

  • Langley, P. (1985). Learning to search: From weak methods to domain specific heuristics. Cognitive Science, 9(2), 217–260.

    Google Scholar 

  • Lavrač, N., & Džeroski, S. (Eds.). (1994). Inductive Logic Programming: Techniques and Applications. Ellis Horwood.

    Google Scholar 

  • Leckie, C., & Zuckerman, I. (1993). An inductive approach to learning search control rules for planning. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1100–1105 Chamberry,France.

    Google Scholar 

  • Lehnert, W., & Sundheim, B. (1991). A performance evaluation of text-analysis technologies. AI Magazine, 12 (3), 81–94.

    Google Scholar 

  • Ling, C. X., & Marinov, M. (1993). Answering the Connectionist challenge: A symbolic model of learning the past tense of English verbs. Cognition, 49(3), 235–290.

    Google Scholar 

  • Magerman, D. M. (1995). Statistical decision-tree models for parsing. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pp. 276–283 Cambridge, MA.

    Google Scholar 

  • Marcus, M. (1980). A Theory of Syntactic Recognition for Natural Language. MIT Press, Cambridge, MA.

    Google Scholar 

  • Marcus, M., Santorini, B., & Marcinkiewicz, M. (1993). Building a large annotated corpus of English: The Penn treebank. Computational Linguistics, 19(2), 313–330.

    Google Scholar 

  • Miikkulainen, R. (1996). Subsymbolic case-role analysis of sentences with embedded clauses. Cognitive Science, 20(1), 47–73.

    Google Scholar 

  • Miikkulainen, R. (1993). Subsymbolic Natural Language Processing: An Integrated Model of Scripts, Lexicon, and Memory. MIT Press, Cambridge, MA.

    Google Scholar 

  • Mitchell, T. (1983). Learning and problem solving. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence, pp. 1139–1151 Karlsruhe, West Germany.

    Google Scholar 

  • Mooney, R. J., & Califf, M. E. (1995). Induction of first-order decision lists: Results on learning the past tense of English verbs. Journal of Artificial Intelligence Research, 3, 1–24.

    Google Scholar 

  • Muggleton, S., & Buntine, W. (1988). Machine invention of first-order predicates by inverting resolution. In Proceedings of the Fifth International Conference on Machine Learning, pp. 339–352 Ann Arbor, MI.

    Google Scholar 

  • Muggleton, S., & Feng, C. (1992). Efficient induction of logic programs. In Muggleton, S. (Ed.), Inductive Logic Programming, pp. 281–297. Academic Press, New York.

    Google Scholar 

  • Periera, F., & Shabes, Y. (1992). Inside-outside reestimation from partially bracketed corpora. In Proceedings of the 30th Annual Meeting of the Association for Computational Linguistics, pp. 128–135 Newark, Delaware.

    Google Scholar 

  • Pinker, S. (Ed.). (1994). The Language Instinct: How the Mind Creates Language. William Morrow, N.Y.

    Google Scholar 

  • Quinlan, J. R. (1996). Learning first-order definitions of functions. Journal of Artificial Intelligence Research, to appear.

    Google Scholar 

  • Quinlan, J. (1990). Learning logical definitions from relations. Machine Learning, 5(3),239–266.

    Google Scholar 

  • Reilly, R. G., & Sharkey, N. E. (Eds.). (1992). Connectionist Approaches to Natural Language Processing. Lawrence Erlbaum and Associates, Hilldale, NJ.

    Google Scholar 

  • Richards, B. L., & Mooney, R. J. (1995). Automated refinement of first-order Hornclause domain theories. Machine Learning, 19(2), 95–131.

    Google Scholar 

  • Riloff, E. (1993). Automatically constructing a dictionary for information extraction tasks. In Proceedings of the Eleventh National Conference on Artificial Intelligence, pp. 811–816.

    Google Scholar 

  • Rumelhart, D. E., & McClelland, J. (1986). On learning the past tense of English verbs. In Rumelhart, D. E., & McClelland, J. L. (Eds.), Parallel Distributed Processing, Vol. II, pp. 216–271. MIT Press, Cambridge, MA.

    Google Scholar 

  • Simmons, R. F., & Yu, Y. (1992). The acquisition and use of context dependent grammars for English. Computational Linguistics, 18(4), 391–418.

    Google Scholar 

  • Soderland, S., & Lehnert, W. (1994). Wrap-Up: A trainable discourse module for information extraction. Journal of Artificial Intelligence Research, 2, 131–158.

    Google Scholar 

  • Tomita, M. (1986). Efficient Parsing for Natural Language. Kluwer Academic Publishers, Boston.

    Google Scholar 

  • Waibel, A., & Lee, K. F. (Eds.). (1990). Readings in Speech Recognition. Morgan Kaufmann, San Mateo,CA.

    Google Scholar 

  • Warren, D., & Pereira, F. (1982). An efficient easily adaptable system for interpreting natural language queries. American Journal of Computational Linguistics, 8(34), 110–122.

    Google Scholar 

  • Wermter, S., Riloff, E., & Scheler, G. (Eds.). (1996). Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing. Springer Verlag, Berlin.

    Google Scholar 

  • Wirth, R. (1988). Learning by failure to prove. In Proceedings of the Third European Working Session on Learning, pp. 237–251. Pitman.

    Google Scholar 

  • Wirth, R. (1989). Completing logic programs by inverse resolution. In Proceedings of the Fourth European Working Session on Learning, pp. 239–250. Pitman.

    Google Scholar 

  • Zelle, J. M. (1995). Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers. Ph.D. thesis, University of Texas, Austin, TX.

    Google Scholar 

  • Zelle, J. M., & Mooney, R. J. (1993a). Combining FOIL and EBG to speed-up logic programs. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1106–1111 Chambery, France.

    Google Scholar 

  • Zelle, J. M., & Mooney, R. J. (1993b). Learning semantic grammars with constructive inductive logic programming. In Proceedings of the Eleventh National Conference on Artificial Intelligence, pp. 817–822 Washington, D.C.

    Google Scholar 

  • Zelle, J. M., & Mooney, R. J. (1994a). Combining top-down and bottom-up methods in inductive logic programming. In Proceedings of the Eleventh International Conference on Machine Learning, pp. 343–351 New Brunswick, NJ.

    Google Scholar 

  • Zelle, J. M., & Mooney, R. J. (1994b). Inducing deterministic Prolog parsers from treebanks: A machine learning approach. In Proceedings of the Twelfth National Conference on Artificial Intelligence, pp. 748–753 Seattle, WA.

    Google Scholar 

  • Zelle, J. M., & Mooney, R. J. (1996a). Comparative results on using inductive logic programming for corpus-based parser construction. In Wermter, S., Riloff, E., & Scheler, G. (Eds.), Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, pp. 355–369. Springer, Berlin.

    Google Scholar 

  • Zelle, J. M., & Mooney, R. J. (1996b). Learning to parse database queries using inductive logic programming. In Proceedings of the Thirteenth National Conference on Artificial Intelligence Portland, OR.

    Google Scholar 

  • Zelle, J. M., Thompson, C., Califf, M. E., & Mooney, R. J. (1995). Inducing logic programs without explicit negative examples. In Proceedings of the Fifth International Workshop on Inductive Logic Programming, pp. 403–416 Leuven, Belgium.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, University of Texas, 78712-1188, Austin, TX, USA

    Raymond J. Mooney

Authors
  1. Raymond J. Mooney
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Stephen Muggleton

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mooney, R.J. (1997). Inductive logic programming for natural language processing. In: Muggleton, S. (eds) Inductive Logic Programming. ILP 1996. Lecture Notes in Computer Science, vol 1314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63494-0_45

Download citation

  • DOI: https://doi.org/10.1007/3-540-63494-0_45

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63494-2

  • Online ISBN: 978-3-540-69583-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation