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Cross-Language Frame Semantics Transfer in Bilingual Corpora

  • Roberto Basili
  • Diego De Cao
  • Danilo Croce
  • Bonaventura Coppola
  • Alessandro Moschitti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5449)

Abstract

Recent work on the transfer of semantic information across languages has been recently applied to the development of resources annotated with Frame information for different non-English European languages. These works are based on the assumption that parallel corpora annotated for English can be used to transfer the semantic information to the other target languages. In this paper, a robust method based on a statistical machine translation step augmented with simple rule-based post-processing is presented. It alleviates problems related to preprocessing errors and the complex optimization required by syntax-dependent models of the cross-lingual mapping. Different alignment strategies are here investigated against the Europarl corpus. Results suggest that the quality of the derived annotations is surprisingly good and well suited for training semantic role labeling systems.

Keywords

Semantic Space Semantic Role Statistical Machine Translation Frame Element Sentence Pair 
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.

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References

  1. 1.
    Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley FrameNet project. In: Proc. of COLING-ACL 1998, pp. 86–90 (1998)Google Scholar
  2. 2.
    Fillmore, C.J.: Frames and the semantics of understanding. Quaderni di Semantica 4(2), 222–254 (1985)Google Scholar
  3. 3.
    Palmer, M., Gildea, D., Kingsbury, P.: The Proposition Bank: an Annotated Corpus of Semantic Roles. Computational Linguistics 31(1), 71–106 (2005)CrossRefGoogle Scholar
  4. 4.
    Gildea, D., Jurafsky, D.: Automatic Labeling of Semantic Roles. Computational Linguistics 28(3), 245–288 (2002)CrossRefGoogle Scholar
  5. 5.
    Carreras, X., Màrquez, L.: Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling. In: Proc. of CoNLL 2005, Ann Arbor, Michigan, pp. 152–164 (2005)Google Scholar
  6. 6.
    Padïo, S.: Cross-lingual annotation projection models for role-semantic information. PhD Thesis, Dissertation, Universität des Saarlandes, Saarbrücken, Germany (2007)Google Scholar
  7. 7.
    Padïo, S., Pitel, G.: Annotation prïecise du francais en sïemantique de roles par projection cross-linguistique. In: Proc. of TALN 2007, Toulouse, France (2007)Google Scholar
  8. 8.
    Tonelli, S., Pianta, E.: Frame information transfer from english to italian. In: Proc. of LREC Conference, Marrakech, Marocco (2008)Google Scholar
  9. 9.
    Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E.: Moses: Open source toolkit for statistical machine translation. In: Annual Meeting of the Association for Computational Linguistics (ACL), Demonstration Session, Prague, Czech Republic (2007)Google Scholar
  10. 10.
    De Cao, D., Croce, D., Pennacchiotti, M., Basili, R.: Combining word sense and usage for modeling frame semantics. In: Proc. of The Symposium on Semantics in Systems for Text Processing (STEP 2008), Venice, Italy, September 22-24 (2008)Google Scholar
  11. 11.
    Roberto, B., De Cao, D., Pennacchiotti, M., Croce, D., Roth, M.: Automatic induction of framenet lexical units. In: Proc. of the 12th International Conference on Empirical Methods for NLP (EMNLP 2008), Honolulu, USA (2008)Google Scholar
  12. 12.
    Heyer, L., Kruglyak, S., Yooseph, S.: Exploring expression data: Identification and analysis of coexpressed genes. Genome Research (9), 1106–1115 (1999)CrossRefGoogle Scholar
  13. 13.
    Landauer, T., Dumais, S.: A solution to plato’s problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review 104, 211–240 (1997)CrossRefGoogle Scholar
  14. 14.
    Koehn, P., Hoang, H.: Factored translation models. In: Proc. of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, Czech Republic, pp. 868–876 (2007)Google Scholar
  15. 15.
    Koehn, P.: Europarl: A parallel corpus for statistical machine translation. In: Proc. of the MT Summit, Phuket, Thailand (2005)Google Scholar
  16. 16.
    Moschitti, A.: Making Tree Kernels Practical for Natural Language Learning. In: Proc. of EACL 2006, pp. 113–120 (2006)Google Scholar
  17. 17.
    Moschitti, A., Pighin, D., Basili, R.: Tree Kernels for Semantic Role Labeling. Computational Linguistics Special Issue on Semantic Role Labeling (3), 245–288 (2008)Google Scholar
  18. 18.
    Coppola, B., Moschitti, A., Pighin, D.: Generalized Framework for Syntax-based Relation Mining. In: Proceedings of the IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Roberto Basili
    • 1
  • Diego De Cao
    • 1
  • Danilo Croce
    • 1
  • Bonaventura Coppola
    • 2
  • Alessandro Moschitti
    • 2
  1. 1.Dept. of Computer ScienceUniversity of Roma Tor VergataRomaItaly
  2. 2.University of TrentoItaly

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