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How can Grammatical Inference Contribute to Computational Linguistics?

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Language, Life, Limits (CiE 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8493))

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Abstract

Grammatical Inference refers to the process of learning grammars and languages from data. Although there are clear connections between Grammatical Inference and Computational Linguistics, there have been a poor interaction between these two fields. The goals of this article are: i) To introduce Grammatical Inference to computational linguists; ii) To explore how Grammatical Inference can contribute to Computational Linguistics.

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References

  1. Adriaans, P.: Language learning from a categorial perspective. PhD thesis, University of Amsterdam (1992)

    Google Scholar 

  2. Angluin, D.: Inference of reversible languages. Journal of the Association for Computing Machinery 29(3), 741–765 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  3. Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 75, 87–106 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  4. Angluin, D., Kharitonov, M.: When won’t membership queries help? In: STOC 1991, pp. 444–454 (1991)

    Google Scholar 

  5. Angluin, D., Becerra-Bonache, L.: Learning meaning before syntax. In: Clark, A., Coste, F., Miclet, L. (eds.) ICGI 2008. LNCS (LNAI), vol. 5278, pp. 1–14. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  6. Angluin, D., Becerra Bonache, L.: Effects of Meaning-Preserving Corrections on Language Learning. In: CoNLL 2011, pp. 97–105 (2011)

    Google Scholar 

  7. Balcázar, J.L., Díaz, J., Gavaldà, R., Watanabe, O.: Algorithms for Learning Finite Automata from Queries: A unified view. In: Du, D.Z., Ko, K.I. (eds.) Advances in Algorithms, Languages, and Complexity, pp. 53–72 (1997)

    Google Scholar 

  8. Becerra-Bonache, L.: On the Learnability of Mildly Context-Sensitive Languages using Positive Data and Correction Queries. PhD thesis, Rovira i Virgili Univ. (2006)

    Google Scholar 

  9. Becerra-Bonache, L., Case, J., Jain, S., Stephan, F.: Iterative learning of simple external contextual languages. Theoretical Computer Science 411, 2741–2756 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  10. Becerra-Bonache, L., de la Higuera, C., Janodet, J.C., Tantini, F.: Learning balls of strings from edit corrections. JMLR 9, 1841–1870 (2008)

    MATH  Google Scholar 

  11. Becerra-Bonache, L., Dediu, A.-H., Tîrnăucă, C.: Learning DFA from correction and equivalence queries. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds.) ICGI 2006. LNCS (LNAI), vol. 4201, pp. 281–292. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  12. Becerra-Bonache, L., Fromont, E., Habrard, A., Perrot, M., Sebban, M.: Speeding up Syntactic Learning Using Contextual Information. In: ICGI 2012, vol. 21, pp. 49–53 (2012)

    Google Scholar 

  13. Bresnan, J., Kaplan, R.M., Peters, S., Zaenen, A.: Cross-serial dependencies in dutch. In: Savitch, W.J., Bach, E., Marsh, W., Safran-Naveh, G. (eds.) The Formal Complexity of Natural Language, pp. 286–319 (1987)

    Google Scholar 

  14. Carrasco, R.C., Oncina, J.: Learning stochastic regular grammars by means of a state merging method. In: Carrasco, R.C., Oncina, J. (eds.) ICGI 1994. LNCS, vol. 862, pp. 139–152. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  15. Casacuberta, F., Vidal, E.: Learning finite-state models for machine translation. Machine Learning 66(1), 69–91 (2007)

    Article  Google Scholar 

  16. Castellanos, A., Vidal, E., Var, M.A., Oncina, J.: Language understanding and subsequential transducer learning. Computer Speech and Language 12(3), 193–228 (1998)

    Article  Google Scholar 

  17. Chouinard, M.M., Clark, E.V.: Adult reformulations of child errors as negative evidence. Journal of Child Language 30, 637–669 (2003)

    Article  Google Scholar 

  18. Clark, A.: Grammatical inference and first language acquisition. In: Workshop on Psychocomputational Models of Human Language Acquisition, pp. 25–32 (2004)

    Google Scholar 

  19. Culy, C.: The complexity of the vocabulary of bambara. In: Savitch, W.J., Bach, E., Marsh, W., Safran-Naveh, G. (eds.) The Formal Complexity of Natural Language, pp. 349–357 (1987)

    Google Scholar 

  20. de la Higuera, C., Thollard, F.: Identification in the limit with probability one of stochastic deterministic finite automata. In: Oliveira, A.L. (ed.) ICGI 2000. LNCS (LNAI), vol. 1891, pp. 15–24. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  21. de la Higuera, C.: Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, Cambridge (2010)

    Google Scholar 

  22. D‘Ulizia, A., Ferri, F., Grifoni, P.: A survey of grammatical inference methods for natural language learning. Artificial Intelligence Review 36(1), 1–27 (2011)

    Article  Google Scholar 

  23. Gold, E.M.: Language identification in the limit. Information and Control 10, 447–474 (1967)

    Article  MATH  Google Scholar 

  24. Ishizaka, H.: Polynomial time learnability of simple deterministic languages. Machine Learning 5, 151–164 (1990)

    Google Scholar 

  25. Joshi, A.K.: How much context-sensitivity is required to provide reasonable structural descriptions: Tree adjoining grammars. In: Dowty, D., Karttunen, L., Zwicky, A. (eds.) Natural Language Parsing: Psychological, Computational and Theoretical Perspectives, pp. 206–250. Cambridge University Press, New York (1985)

    Google Scholar 

  26. Joshi, A.K., Shanker, K.V., Weir, D.: The Convergence of Mildly Context-Sensitive Grammar Formalisms. In: Technical Report, University of Pennsylvania (1990)

    Google Scholar 

  27. Kinber, E.: On learning regular expressions and patterns via membership and correction queries. In: Clark, A., Coste, F., Miclet, L. (eds.) ICGI 2008. LNCS (LNAI), vol. 5278, pp. 125–138. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  28. Kudlek, M., Martín-Vide, C., Mateescu, A., Mitrana, V.: Contexts and the concept of mild context-sensitivity. Linguistics and Philosophy 26(6), 703–725 (2002)

    Article  Google Scholar 

  29. Manaster-Ramer, A.: Some uses and abuses of mathematics in linguistics. In: Martín-Vide, C. (ed.) Issues in Mathematical Linguistics, pp. 73–130. John Benjamins, Amsterdam (1999)

    Chapter  Google Scholar 

  30. Oncina, J., García, P.: Identifying regular languages in polynomial time. In: Bunke, H. (ed.) Advances in Structural and Syntactic Pattern Recognition, vol. 5, pp. 99–108 (1992)

    Google Scholar 

  31. Oncina, J., García, P., Vidal, E.: Learning subsequential transducers for pattern recognition interpretation tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(5), 448–458 (1993)

    Article  Google Scholar 

  32. Rozenberg, G., Salomaa, A.: Handbook of formal languages. Springer (1997)

    Google Scholar 

  33. Sakakibara, Y.: Learning context-free grammars from structural data in polynomial time. Theoretical Computer Science 76, 223–242 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  34. Sakakibara, Y.: Efficient learning of context-free grammars from positive structural examples. Information Processing Letters 97, 23–60 (1992)

    MATH  MathSciNet  Google Scholar 

  35. Sempere, J.M., García, P.: A characterization of even linear languages and its application to the learning problem. In: Carrasco, R.C., Oncina, J. (eds.) ICGI 1994. LNCS, vol. 862, pp. 38–44. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  36. Shieber, S.M.: Evidence against the context-freeness of natural languages. In: Savitch, W.J., Bach, E., Marsh, W., Safran-Naveh, G. (eds.) The Formal Complexity of Natural Language, pp. 320–334. D. Reidel, Dordrecht (1987)

    Google Scholar 

  37. Solan, Z., Horn, D., Ruppin, E., Edelman, S.: Unsupervised learning of natural languages. PNAS 102(33), 11629–11634 (2005)

    Article  Google Scholar 

  38. Takada, Y.: Grammatical inference for even linear languages based on control sets. Information Processing Letters 28(4), 193–199 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  39. Tîrnăucă, C., Knuutila, T.: Polynomial time algorithms for learning k-reversible languages and pattern languages with correction queries. In: Hutter, M., Servedio, R.A., Takimoto, E. (eds.) ALT 2007. LNCS (LNAI), vol. 4754, pp. 272–284. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  40. Valiant, L.G.: A theory of the learnable. Communication of the ACM 27, 1134–1142 (1984)

    Article  MATH  Google Scholar 

  41. van Zaanen, M.M.V.: Bootstrapping structure into language: alignment-based learning. PhD thesis, University of Leeds (2001)

    Google Scholar 

  42. Yoshinaka, R.: Learning mildly context-sensitive languages with multidimensional substitutability from positive data. In: Gavaldà, R., Lugosi, G., Zeugmann, T., Zilles, S. (eds.) ALT 2009. LNCS, vol. 5809, pp. 278–292. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratoire Hubert Curien, Université Jean Monnet, 18 rue du Professeur Benoit Lauras, 42000, Saint Etienne, France

    Leonor Becerra-Bonache

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  1. Leonor Becerra-Bonache
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Editor information

Editors and Affiliations

  1. College of Science, Department of Computer Science, Swansea University, SA2 8PP, Swansea, UK

    Arnold Beckmann

  2. Department of Algorithms and Their Applications, Eötvös Loránd University, Pázmány Péter sétány 1/c,, 1117, Budapest, Hungary

    Erzsébet Csuhaj-Varjú

  3. Computer Science Institute, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany

    Klaus Meer

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Becerra-Bonache, L. (2014). How can Grammatical Inference Contribute to Computational Linguistics?. In: Beckmann, A., Csuhaj-Varjú, E., Meer, K. (eds) Language, Life, Limits. CiE 2014. Lecture Notes in Computer Science, vol 8493. Springer, Cham. https://doi.org/10.1007/978-3-319-08019-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-08019-2_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08018-5

  • Online ISBN: 978-3-319-08019-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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