Ontology-Based Semantic Interpretation via Grammar Constraints

Chapter
Part of the Theory and Applications of Natural Language Processing book series (NLP)

Abstract

We present an ontology-based semantic interpreter that can be linked to a grammar through grammar rule constraints, providing access to meaning during language processing. In this approach, the parser will take as input natural language utterances and will produce ontology-based semantic representations. We rely on a recently developed constraint-based grammar formalism, which balances expressiveness with practical learnability results. We show that even with a lightweight ontology, the semantic interpreter at the grammar rule level can help remove erroneous parses obtained when we do not have access to meaning.

Keywords

Semantic Representation Relative Clause Semantic Model Semantic Interpretation Inductive Logic Programming 
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.

Notes

Acknowledgements

The author acknowledges the support of the National Science Foundation (IIS-1065195). The author thanks the anonymous reviewers for their feedback. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the author, and do not necessarily reflect the views of the funding organization.

References

  1. 1.
    Basili, R., Hansen, D.H., Paggio, P., Pazienza, M.T., Zanzotto, F.: Ontological resources and question answering. In: Workshop on Pragmatics of Question Answering, Held Jointly with NAACL 2004, Boston (2004)Google Scholar
  2. 2.
    Beale, S., Lavoie, B., McShane, M., Nirenburg, S., Korelsky, T.: Question answering using ontological semantics. In: ACL 2004: Second Workshop on Text Meaning and Interpretation, Barcelona (2004)Google Scholar
  3. 3.
    Bresnan, J.: Lexical-Functional Syntax. Blackwell, Oxford (2001)Google Scholar
  4. 4.
    Charniak, E.: A maximum-entropy-inspired parser. In: Proceedings of the first conference on North American chapter of the Association for Computational Linguistics (NAACL-2000), Seattle (2000)Google Scholar
  5. 5.
    Collins, M.: Head-driven statistical models for natural language parsing. Ph.D. thesis, University of Pennsylvania (1999)Google Scholar
  6. 6.
    Domingos, P., Richardson, M.: Markov logic: a unifying framework for statistical relational learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning, pp. 339–371. MIT, Cambridge (2007)Google Scholar
  7. 7.
    Dorr, B.J.: Large-scale dictionary construction for foreign language tutoring and interlingual machine translation. Mach. Trans. 12(4), 271–322 (1997)CrossRefGoogle Scholar
  8. 8.
    Dzeroski, S.: Inductive logic programming in a nutshell. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT, Cambridge (2007)Google Scholar
  9. 9.
    Freivalds, R., Kinber, E.B., Wiehagen, R.: On the power of inductive inference from good examples. Theor. Comput. Sci. 110(1), 131–144 (1993)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Ge, R., Mooney, R.J.: A statistical semantic parser that integrates syntax and semantics. In: Proceedings of CoNLL-2005, Ann Arbor (2005)Google Scholar
  11. 11.
    He, Y., Young, S.: Spoken language understanding using the hidden vector state model. Speech Commun. 48(3–4), 262–275 (2006). Special issue on spoken language understanding in conversational systemsGoogle Scholar
  12. 12.
    Hirst, G.: Ontology and the lexicon. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies in Information Systems. Springer, Berlin (2003)Google Scholar
  13. 13.
    Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., Weischedel, R.: Ontonotes: the 90 % solution. In: Proceedings of HLT-NAACL 2006, New York (2006)Google Scholar
  14. 14.
    Jensen, P.A., Nilsson, J.F.: Ontology-based semantics of prepositions. In: Proceedings of ACL-SIGSEM Workshop: The Linguistic Dimensions of Prepositions and their Use in Computational Linguistics Formalisms and Applications, Toulouse (2003)Google Scholar
  15. 15.
    Joshi, A., Schabes, Y.: Tree-adjoining grammars. In: Rozenberg, G., Salomaa, A. (eds.) Handbook of Formal Languages, vol. 3, chap. 2, pp. 69–124. Springer, Berlin/New York (1997)CrossRefGoogle Scholar
  16. 16.
    Kaplan, R., Bresnan, J.: Lexical-functional grammar: a formal system for grammatical representation. In: Bresnan, J. (ed.) The Mental Representation of Grammatical Relations, pp. 173–281. MIT, Cambridge (1982)Google Scholar
  17. 17.
    Klavans, J., Muresan, S.: Evaluation of DEFINDER: a system to mine definitions from consumer-oriented medical text. In: Proceedings of The First ACM+IEEE Joint Conference on Digital Libraries, Roanoke (2001)Google Scholar
  18. 18.
    Kowalski, R.A.: Logic for Problem Solving. North-Holland, Amsterdam (1979)MATHGoogle Scholar
  19. 19.
    Miller, G.: WordNet: an on-line lexical database. J. Lexicogr. 3(4), 235–312 (1990)CrossRefGoogle Scholar
  20. 20.
    Muresan, S.: Learning constraint-based grammars from representative examples: theory and applications. Tech. rep., Ph.D. Thesis, Columbia University (2006)Google Scholar
  21. 21.
    Muresan, S.: Learning to map text to graph-based meaning representations via grammar induction. In: Coling 2008: Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing, Manchester, pp. 9–16 (2008)Google Scholar
  22. 22.
    Muresan, S.: A learnable constraint-based grammar formalism. In: Proceedings of COLING, Beijing (2010)Google Scholar
  23. 23.
    Muresan, S.: Learning for deep language understanding. In: Proceedings of IJCAI-11, Barcelona (2011)Google Scholar
  24. 24.
    Muresan, S., Klavans, J.L.: A method for automatically building and evaluating dictionary resources. In: Proceedings of the Language Resources and Evaluation Conference (LREC-2002), Las Palmas (2002)Google Scholar
  25. 25.
    Muresan, S., Rambow, O.: Grammar approximation by representative sublanguage: a new model for language learning. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), Prague (2007)Google Scholar
  26. 26.
    Nirenburg, S., Raskin, V.: Ontological Semantics. MIT, Cambridge (2004)Google Scholar
  27. 27.
    Pereira, F.C., Warren, D.H.: Definite Clause Grammars for language analysis. Artif. Intell. 13, 231–278 (1980)MathSciNetMATHCrossRefGoogle Scholar
  28. 28.
    Pollard, C., Sag, I.: Head-Driven Phrase Structure Grammar. University of Chicago Press, Chicago (1994)Google Scholar
  29. 29.
    Poon, H., Domingos, P.: Unsupervised semantic parsing. In: Proceedings of EMNLP’09, Singapore (2009)Google Scholar
  30. 30.
    Poon, H., Domingos, P.: Unsupervised ontology induction from text. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL ’10), pp. 296–305. Association for Computational Linguistics, Stroudsburg, PA, USA (2010)Google Scholar
  31. 31.
    Saraswat, V.: Concurrent constraint programming languages. Ph.D. thesis, Department of Computer Science, Carnegie Mellon University (1989)Google Scholar
  32. 32.
    Shieber, S.: The problem of logical-form equivalence. Comput. Linguist. 19(1), 179–190 (1994)Google Scholar
  33. 33.
    Shieber, S., Uszkoreit, H., Pereira, F., Robinson, J., Tyson, M.: The formalism and implementation of PATR-II. In: Grosz, B.J., Stickel, M. (eds.) Research on Interactive Acquisition and Use of Knowledge, pp. 39–79. SRI International, Menlo Park (1983)Google Scholar
  34. 34.
    Sowa, J.F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks Cole Publishing, Pacific Grove (1999)Google Scholar
  35. 35.
    Steedman, M.: Surface Structure and Interpretation. MIT, Cambridge (1996)Google Scholar
  36. 36.
    Wong, Y.W., Mooney, R.: Learning synchronous grammars for semantic parsing with lambda calculus. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-2007), Prague (2007)Google Scholar
  37. 37.
    Zettlemoyer, L.S., Collins, M.: Learning to map sentences to logical form: structured classification with probabilistic categorial grammars. In: Proceedings of UAI-05, Edinburgh (2005)Google Scholar
  38. 38.
    Zettlemoyer, L.S., Collins, M.: Learning context-dependent mappings from sentences to logical form. In: Proceedings of the Association for Computational Linguistics (ACL’09), Singapore (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Communication and InformationRutgers UniversityNew BrunswickUSA

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