Flexible Combinatory Categorial Grammar Parsing Using the CYK Algorithm and Answer Set Programming

  • Peter Schüller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8148)

Abstract

Combinatory Categorial Grammar (CCG) is a grammar formalism used for natural language parsing. CCG assigns structured lexical categories to words and uses a small set of combinatory rules to combine these categories in order to parse sentences. In this work we describe and implement a new approach to CCG parsing that relies on Answer Set Programming (ASP) — a declarative programming paradigm.Different from previous work, we present an encoding that is inspired by the algorithm due to Cocke, Younger, and Kasami (CYK). We also show encoding extensions for parse tree normalization and best-effort parsing and outline possible future extensions which are possible due to the usage of ASP as computational mechanism. We analyze performance of our approach on a part of the Brown corpus and discuss lessons learned during experiments with the ASP tools dlv, gringo, and clasp. The new approach is available in the open source CCG parsing toolkit AspCcgTk which uses the C&C supertagger as a preprocessor to achieve wide-coverage natural language parsing.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alviano, M., Faber, W., Greco, G., Leone, N.: Magic sets for disjunctive datalog programs. Tech. rep., Università della Calabria, Dipartimento di Matematica (2009)Google Scholar
  2. 2.
    Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley FrameNet Project. In: 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, pp. 86–90 (1998)Google Scholar
  3. 3.
    Baral, C.: Knowledge Representation, Reasoning, and Declarative Problem Solving. Cambridge University Press (2003)Google Scholar
  4. 4.
    Beavers, J.: Documentation: A CCG implementation for the LKB. Tech. rep., Stanford University, Center for the Study of Language and Information (2003)Google Scholar
  5. 5.
    Beavers, J.: Type-inheritance combinatory categorial grammar. In: International Conference on Computational Linguistics, COLING 2004 (2004)Google Scholar
  6. 6.
    Bos, J.: Wide-coverage semantic analysis with boxer. In: Semantics in Text Processing, STEP, pp. 277–286. College Publications (2008)Google Scholar
  7. 7.
    Brewka, G., Eiter, T., Truszczynski, M.: Answer set programming at a glance. Commun. ACM 54(12), 92–103 (2011)CrossRefGoogle Scholar
  8. 8.
    Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Ricca, F., Schaub, T.: ASP-Core-2 input language format (2012)Google Scholar
  9. 9.
    Clark, S., Curran, J.R.: Log-linear models for wide-coverage CCG parsing. In: SIGDAT Conference on Empirical Methods in Natural Language Processing, EMNLP 2003 (2003)Google Scholar
  10. 10.
    Clark, S., Curran, J.R.: Parsing the WSJ using CCG and log-linear models. In: 42nd Annual Meeting of the Association for Computational Linguistics, ACL, pp. 104–111 (2004)Google Scholar
  11. 11.
    Clark, S., Curran, J.R.: Wide-coverage efficient statistical parsing with CCG and log-linear models. Computational Linguistics 33(4), 493–552 (2007)CrossRefMATHGoogle Scholar
  12. 12.
    Cocke, J., Schwartz, J.T.: Programming Languages and Their Compilers. Courant Institute of Mathematical Sciences, New York (1970)MATHGoogle Scholar
  13. 13.
    Djordjevic, B., Curran, J.R.: Efficient combinatory categorial grammar parsing. In: Proceedings of the 2006 Australasian Language Technology Workshop, ALTW, pp. 3–10 (2006)Google Scholar
  14. 14.
    Drescher, C., Walsh, T.: Modelling grammar constraints with answer set programming. In: International Conference on Logic Programming, vol. 11, pp. 28–39 (2011)Google Scholar
  15. 15.
    Eisner, J.: Efficient normal-form parsing for combinatory categorial grammar. In: 34th Annual Meeting of the Association for Computational Linguistics, pp. 79–86. ACL (1996)Google Scholar
  16. 16.
    Francis, W.N., Kucera, H.: Brown corpus manual. Letters to the Editor 5(2), 7 (1979)Google Scholar
  17. 17.
    Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: Conflict-driven answer set solving. In: International Joint Conference on Artificial Intelligence, pp. 386–392 (2007)Google Scholar
  18. 18.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proc. International Logic Programming Conference and Symposium, ICLP, pp. 1070–1080 (1988)Google Scholar
  19. 19.
    Kasami, T.: An efficient recognition and syntax analysis algorithm for context-free languages. Tech. Rep. AFCRL-65-758, Air Force Cambridge Research Laboratory (1965)Google Scholar
  20. 20.
    Katsirelos, G., Narodytska, N., Walsh, T.: Reformulating global grammar constraints. In: van Hoeve, W.-J., Hooker, J.N. (eds.) CPAIOR 2009. LNCS, vol. 5547, pp. 132–147. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  21. 21.
    Lange, M., Leiß, H.: To CNF or not to CNF? An efficient yet presentable version of the CYK algorithm. In: Informatica Didactica 8. Universität Potsdam (2009)Google Scholar
  22. 22.
    Lierler, Y., Schüller, P.: Parsing Combinatory Categorial Grammar via planning in Answer Set Programming. In: Erdem, E., Lee, J., Lierler, Y., Pearce, D. (eds.) Correct Reasoning. LNCS, vol. 7265, pp. 436–453. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  23. 23.
    Lierler, Y., Schüller, P.: Towards a tight integration of syntactic parsing with semantic disambiguation by means of declarative programming. In: Erk, K., Koller, A. (eds.) 10th International Conference on Computational Semantics (2013)Google Scholar
  24. 24.
    Lifschitz, V.: Answer set programming and plan generation. Artif. Intel. 138, 39–54 (2002)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Marek, V., Truszczyński, M.: Stable models and an alternative logic programming paradigm. In: The Logic Programming Paradigm: A 25-Year Perspective, pp. 375–398. Springer (1999)Google Scholar
  26. 26.
    Moot, R.: Proof Nets for Linguistic Analysis. Ph.D. thesis, Utrecht Institute of Linguistics OTS (2002)Google Scholar
  27. 27.
    Niemelä, I.: Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 241–273 (1999)MathSciNetCrossRefMATHGoogle Scholar
  28. 28.
    Quimper, C.-G., Walsh, T.: Decomposing global grammar constraints. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 590–604. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  29. 29.
    Steedman, M.: The syntactic process. MIT Press, London (2000)Google Scholar
  30. 30.
    Vijay-Shanker, K., Weir, D.J.: Polynomial time parsing of combinatory categorial grammars. In: 28th Annual Meeting of the Association for Computational Linguistics, pp. 1–8 (1990)Google Scholar
  31. 31.
    White, M., Baldridge, J.: Adapting chart realization to CCG. In: European Workshop on Natural Language Generation, EWNLG 2003 (2003)Google Scholar
  32. 32.
    Wittenburg, K.: Predictive combinators: a method for efficient processing of combinatory categorial grammars. In: 25th Annual Meeting of the Association for Computational Linguistics, ACL, pp. 73–80 (1987)Google Scholar
  33. 33.
    Younger, D.H.: Recognition and parsing of context-free languages in time n 3. Information and Control 10(2), 189–208 (1967)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peter Schüller
    • 1
  1. 1.Cognitive Robotics LaboratorySabancı UniversityTurkey

Personalised recommendations