Advertisement

Semantic Role Labelling without Deep Syntactic Parsing

  • Konrad Gołuchowski
  • Adam Przepiórkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7614)

Abstract

This article proposes a method of Semantic Role Labelling for languages with no reliable deep syntactic parser and with limited corpora annotated with semantic roles. Reasonable results may be achieved with the help of shallow parsing, provided that features used for training such shallow parsers include both lexical semantic information (here: hypernymy) and syntactic information.

Keywords

argument identification semantic role classification shallow parsing chunking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abney, S.: Parsing by chunks. In: Berwick, R., Abney, S., Tenny, C. (eds.) Principle-Based Parsing, pp. 257–278. Kluwer (1991)Google Scholar
  2. 2.
    Fleischman, M., Kwon, N., Hovy, E.: Maximum entropy models for framenet classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2003)Google Scholar
  3. 3.
    Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Computational Linguistics 28(3), 245–288 (2002)CrossRefGoogle Scholar
  4. 4.
    Głowińska, K.: Anotacja składniowa NKJP. In: Przepiórkowski, A., Bańko, M., Górski, R.L., Lewandowska-Tomaszczyk, B. (eds.) Narodowy Korpus Języka Polskiego. Wydawnictwo Naukowe PWN, Warsaw (2012)Google Scholar
  5. 5.
    Głowińska, K., Przepiórkowski, A.: The design of syntactic annotation levels in the National Corpus of Polish. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation, LREC 2010. ELRA, Valletta (2010)Google Scholar
  6. 6.
    Hacioglu, K., Pradhan, S., Ward, W., Martin, J.H., Jurafsky, D.: Semantic Role Labeling by Tagging Syntactic Chunks. In: Proceedings of CoNLL 2004, pp. 110–113 (2004)Google Scholar
  7. 7.
    Johansson, R., Nugues, P.: A FrameNet-based semantic role labeler for Swedish. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, COLING-ACL 2006, pp. 436–443. Association for Computational Linguistics, Stroudsburg (2006)CrossRefGoogle Scholar
  8. 8.
    Johnson, C.R., Fillmore, C.J., Petruck, M.R., Baker, C.F., Ellsworth, M.J., Ruppenhofer, J., Wood, E.J.: FrameNet: Theory and Practice (2002)Google Scholar
  9. 9.
    Maziarz, M., Piasecki, M., Szpakowicz, S.: Approaching plWordNet 2.0. In: Proceedings of the 6th Global Wordnet Conference, Matsue, Japan (2012)Google Scholar
  10. 10.
    Mykowiecka, A., Marasek, K., Marciniak, M., Rabiega-Wiśniewska, J., Gubrynowicz, R.: Annotated Corpus of Polish Spoken Dialogues. In: Vetulani, Z., Uszkoreit, H. (eds.) LTC 2007. LNCS, vol. 5603, pp. 50–62. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Palmer, M.: SemLink—linking PropBank, VerbNet, FrameNet and WordNet. In: Proceedings of the Generative Lexicon Conference, Pisa, Italy (2009)Google Scholar
  12. 12.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)Google Scholar
  13. 13.
    Piasecki, M., Szpakowicz, S., Broda, B.: A Wordnet from the Ground Up. Oficyna Wydawnicza Politechniki Wroclawskiej, Wrocław (2009)Google Scholar
  14. 14.
    Pradhan, S., Hacioglu, K., Krugler, V., Ward, W., Martin, J.H., Jurafsky, D.: Support vector learning for semantic argument classification. Machine Learning 60(1-3), 11–39 (2005)CrossRefGoogle Scholar
  15. 15.
    Punyakanok, V., Roth, D., Yih, W.T.: The importance of syntactic parsing and inference in semantic role labeling. Computational Linguistics 34(2), 257–287 (2008)CrossRefGoogle Scholar
  16. 16.
    Ramshaw, L.A., Marcus, M.P.: Text chunking using transformation-based learning. In: Proceedings of the Third Workshop on Very Large Corpora, pp. 82–94. ACL, Cambridge (1995)Google Scholar
  17. 17.
    Schuler, K.K.: VerbNet: A Broad-Coverage, Comprehensive Verb Lexicon. Ph.D. thesis, University of Pennsylvania (2006)Google Scholar
  18. 18.
    Sun, W., Sui, Z., Wang, M., Wang, X.: Chinese semantic role labeling with shallow parsing. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, vol. 3, pp. 1475–1483. Association for Computational Linguistics, Stroudsburg (2009)CrossRefGoogle Scholar
  19. 19.
    Surdeanu, M., Harabagiu, S., Williams, J., Aarseth, P.: Using predicate-argument structures for information extraction. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, ACL 2003, vol. 1, pp. 8–15. Association for Computational Linguistics, Stroudsburg (2003)CrossRefGoogle Scholar
  20. 20.
    Toutanova, K., Haghighi, A., Manning, C.D.: A global joint model for semantic role labeling. Computational Linguistics 34(2), 161–191 (2008)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Xue, N.: Calibrating features for semantic role labeling. In: Proceedings of EMNLP 2004, pp. 88–94 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Konrad Gołuchowski
    • 1
    • 2
  • Adam Przepiórkowski
    • 2
    • 1
  1. 1.University of WarsawPoland
  2. 2.Institute of Computer SciencePolish Academy of SciencesPoland

Personalised recommendations