Learning Co-relations of Plausible Verb Arguments with a WSM and a Distributional Thesaurus

  • Hiram Calvo
  • Kentaro Inui
  • Yuji Matsumoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


We propose a model based on the Word Space Model for calculating the plausibility of candidate arguments given one verb and one argument. The resulting information can be used in co-reference resolution, zero-pronoun resolution or syntactic ambiguity tasks. Previous work such as Selectional Preferences or Semantic Frames acquisition focuses on this task using supervised resources, or predicting arguments independently from each other. On this work we explore the extraction of plausible arguments considering their co-relation, and using no more information than that provided by the dependency parser. This creates a data sparseness problem alleviated by using a distributional thesaurus built from the same data for smoothing. We compare our model with the traditional PLSI method.


Semantic Role Selectional Preference Probabilistic Latent Semantic Analysis Human Language Technology Dependency Parser 
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.


  1. 1.
    Agirre, E., Martinez, D.: Learning class-to-class selectional preferences. In: Workshop on Computational Natural Language Learning, ACL (2001)Google Scholar
  2. 2.
    Bolshakov, I.A., Galicia-Haro, S.N., Gelbukh, A.F.: Detection and Correction of Malapropisms in Spanish by Means of Internet Search. In: Matoušek, V., Mautner, P., Pavelka, T. (eds.) TSD 2005. LNCS (LNAI), vol. 3658, pp. 115–122. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Budanitsky, E., Graeme, H.: Semantic distance in WorldNet: An experimental, application-oriented evaluation of five measures. In: NAACL Workshop on WordNet and other lexical resources (2001)Google Scholar
  4. 4.
    Calvo, H., Gelbukh, A., Kilgarriff, A.: Automatic Thesaurus vs. WordNet: A Comparison of Backoff Techniques for Unsupervised PP Attachment. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 177–188. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Lin, D.: Automatic Retrieval and Clustering of Similar Words. In: Procs. 36th Annual Meeting of the ACL and 17th International Conference on Computational Linguistics (1998)Google Scholar
  6. 6.
    Lin, D.: Dependency-based Evaluation of MINIPAR. In: Proc. Workshop on the Evaluation of Parsing Systems (1998)Google Scholar
  7. 7.
    Fujii, A., Iwayama, M. (eds.): Patent Retrieval Task (PATENT). Fifth NTCIR Workshop Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answering and Cross-Lingual Information Access (2005)Google Scholar
  8. 8.
    Hoffmann, T.: Probabilistic Latent Semantic Analysis, Uncertainity in Artificial Intelligence, UAI (1999)Google Scholar
  9. 9.
    Kawahara, D., Kurohashi, S.: Japanese Case Frame Construction by Coupling the Verb and its Closest Case Component. In: 1st Intl. Conf. on Human Language Technology Research, ACL (2001)Google Scholar
  10. 10.
    Lee, L.: Measures of Distributional Similarity. In: Procs. 37th ACL (1999)Google Scholar
  11. 11.
    McCarthy, D., Carroll, J.: Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences. Computational Linguistics 29(4), 639–654 (2006)CrossRefGoogle Scholar
  12. 12.
    McCarthy, D., Koeling, R., Weeds, J., Carroll, J.: Finding predominant senses in untagged text. In: Procs. 42nd meeting of the ACL, pp. 280–287 (2004)Google Scholar
  13. 13.
    Padó, S., Lapata, M.: Dependency-Based Construction of Semantic Space Models. Computational Linguistics 33(2), 161–199 (2007)CrossRefGoogle Scholar
  14. 14.
    Padó, U.M., Crocker, F., Keller, F.: Modelling Semantic Role Plausibility in Human Sentence Processing. In: Procs. EACL (2006)Google Scholar
  15. 15.
    Ponzetto, P.S., Strube, M.: Exploiting Semantic Role Labeling, WordNet and Wikipedia for Coreference Resolution. In: Procs. Human Language Technology Conference, NAC- ACL, pp. 192–199 (2006)Google Scholar
  16. 16.
    Resnik, P.: Selectional Constraints: An Information-Theoretic Model and its Computational Realization. Cognition 61, 127–159 (1996)CrossRefGoogle Scholar
  17. 17.
    Salgueiro, P., Alexandre, T., Marcu, D., Volpe Nunes, M.: Unsupervised Learning of Verb Argument Structures. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 59–70. Springer, Heidelberg (2006)Google Scholar
  18. 18.
    Tejada, J., Gelbukh, A., Calvo, H.: An Innovative Two-Stage WSD Unsupervised Method. SEPLN Journal 40 (March 2008)Google Scholar
  19. 19.
    Tejada, J., Gelbukh, A., Calvo, H.: Unsupervised WSD with a Dynamic Thesaurus. In: 11th International Conference on Text, Speech and Dialogue. TSD 2008, Brno, Czech Republic, September 8–12 (2008)Google Scholar
  20. 20.
    Weeds, J., Weir, D.: A General Framework for Distributional Similarity. In: Procs. conf. on EMNLP, vol. 10, pp. 81–88 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hiram Calvo
    • 1
    • 2
  • Kentaro Inui
    • 2
  • Yuji Matsumoto
    • 2
  1. 1.Center for Computing ResearchNational Polytechnic InstituteMexico
  2. 2.Nara Institute of Science and TechnologyNaraJapan

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