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Ontology-Based Semantic Interpretation via Grammar Constraints

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New Trends of Research in Ontologies and Lexical Resources

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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.

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Notes

  1. 1.

    We call the parser robust since when no full parse is possible it returns the minimum number of chunks.

  2. 2.

    Lexicalized Well-Founded Grammars are reversible grammars.

  3. 3.

    Starting from a skeleton ontology, generative ontologies are formed by rules for combining concepts using semantic roles (binary relations) as binders: “The role relations express possible relations among the nodes in the lattice constituting the ontology. Thereby they make possible the generation of an infinite number of ontological nodes in the lattice, thus establishing a generative ontology.” [14]

References

  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. 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. Bresnan, J.: Lexical-Functional Syntax. Blackwell, Oxford (2001)

    Google Scholar 

  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. Collins, M.: Head-driven statistical models for natural language parsing. Ph.D. thesis, University of Pennsylvania (1999)

    Google Scholar 

  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. Dorr, B.J.: Large-scale dictionary construction for foreign language tutoring and interlingual machine translation. Mach. Trans. 12(4), 271–322 (1997)

    Article  Google Scholar 

  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. Freivalds, R., Kinber, E.B., Wiehagen, R.: On the power of inductive inference from good examples. Theor. Comput. Sci. 110(1), 131–144 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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 systems

    Google Scholar 

  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. 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. 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. 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)

    Chapter  Google Scholar 

  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. 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. Kowalski, R.A.: Logic for Problem Solving. North-Holland, Amsterdam (1979)

    MATH  Google Scholar 

  19. Miller, G.: WordNet: an on-line lexical database. J. Lexicogr. 3(4), 235–312 (1990)

    Article  Google Scholar 

  20. Muresan, S.: Learning constraint-based grammars from representative examples: theory and applications. Tech. rep., Ph.D. Thesis, Columbia University (2006)

    Google Scholar 

  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. Muresan, S.: A learnable constraint-based grammar formalism. In: Proceedings of COLING, Beijing (2010)

    Google Scholar 

  23. Muresan, S.: Learning for deep language understanding. In: Proceedings of IJCAI-11, Barcelona (2011)

    Google Scholar 

  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. 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. Nirenburg, S., Raskin, V.: Ontological Semantics. MIT, Cambridge (2004)

    Google Scholar 

  27. Pereira, F.C., Warren, D.H.: Definite Clause Grammars for language analysis. Artif. Intell. 13, 231–278 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  28. Pollard, C., Sag, I.: Head-Driven Phrase Structure Grammar. University of Chicago Press, Chicago (1994)

    Google Scholar 

  29. Poon, H., Domingos, P.: Unsupervised semantic parsing. In: Proceedings of EMNLP’09, Singapore (2009)

    Google Scholar 

  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. Saraswat, V.: Concurrent constraint programming languages. Ph.D. thesis, Department of Computer Science, Carnegie Mellon University (1989)

    Google Scholar 

  32. Shieber, S.: The problem of logical-form equivalence. Comput. Linguist. 19(1), 179–190 (1994)

    Google Scholar 

  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. Sowa, J.F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks Cole Publishing, Pacific Grove (1999)

    Google Scholar 

  35. Steedman, M.: Surface Structure and Interpretation. MIT, Cambridge (1996)

    Google Scholar 

  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. 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. 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 

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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.

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Authors and Affiliations

  1. School of Communication and Information, Rutgers University, New Brunswick, NJ, 08901, USA

    Smaranda Muresan

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  1. Smaranda Muresan
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Correspondence to Smaranda Muresan .

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Editors and Affiliations

  1. Psychology Department, Carnegie Mellon University, Forbes Avenue 5000, Pittsburgh, 15213, Pennsylvania, USA

    Alessandro Oltramari

  2. Faculty of Arts, Language, Cognition and, Vrije University Amsterdam, De Boelelaan 1105, Amsterdam, 1081, Netherlands

    Piek Vossen

  3. Department of Computing, Hong Kong Polytechnic University, PQ806 Mong Man Wai Building, Hong Kong, 999077, Hong Kong SAR

    Lu Qin

  4. Language Technologies Institute, Carnegie Mellon University, Forbes Avenue 5000, Marina Del Rey, 15213, Pennsylvania, USA

    Eduard Hovy

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Muresan, S. (2013). Ontology-Based Semantic Interpretation via Grammar Constraints. In: Oltramari, A., Vossen, P., Qin, L., Hovy, E. (eds) New Trends of Research in Ontologies and Lexical Resources. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31782-8_10

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  • DOI: https://doi.org/10.1007/978-3-642-31782-8_10

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