Advertisement

A Linguistic Inspection of Textual Entailment

  • Maria Teresa Pazienza
  • Marco Pennacchiotti
  • Fabio Massimo Zanzotto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3673)

Abstract

Recognition of textual entailment is not an easy task. In fact, early experimental evidences in [1] seems to demonstrate that even human judges often fail in reaching an agreement on the existence of entailment relation between two expressions. We aim to contribute to the theoretical and practical setting of textual entailment, through both a linguistic inspection of the textual entailment phenomenon and the description of a new promising approach to recognition, as implemented in the system we proposed at the RTE competition [2].

Keywords

Semantic Similarity Graph Match Question Answering Graph Isomorphism Entailment Relation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Szpektor, I., Tanev, H., Dagan, I., Coppola, B.: Scaling web-based acquisition of entailment relations. In: Lin, D., Wu, D. (eds.) Proceedings of EMNLP 2004, Barcelona, Spain, pp. 41–48. Association for Computational Linguistics (2004)Google Scholar
  2. 2.
    Dagan, I., Glickman, O., Magnini, B.: The pascal recognising textual entailment challenge. In: PASCAL Challenges Workshop, Southampton, UK (2005)Google Scholar
  3. 3.
    Dagan, I., Glickman, O.: Probabilistic textual entailment: Generic applied modeling of language variability. In: Learning Methods for Text Understanding and Mining, Grenoble, France (2004)Google Scholar
  4. 4.
    Miller, G., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: Five papers on wordnet. Technical Report CSL Report 43, Princeton University (1990)Google Scholar
  5. 5.
    Voorhees, E.M.: Overview of the trec 2003 question answering track. In: TREC, pp. 54–68 (2003)Google Scholar
  6. 6.
    Proceedings of the Seventh Message Understanding Conference (MUC-7), Virginia USA. Morgan Kaufmann, San Francisco (1998)Google Scholar
  7. 7.
    Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M.: Textual entailment as syntactic graph distance: a rule based and a svm based approach. In: PASCAL Challenges Workshop, Southampton, UK (2005)Google Scholar
  8. 8.
    Barzilay, R., McKeown, K.: Extracting paraphrases from a parallel corpus. In: Proceedings of the 39th ACL Meeting, Toulouse, France (2001)Google Scholar
  9. 9.
    Lin, D., Pantel, P.: DIRT, discovery of inference rules from text. In: Knowledge Discovery and Data Mining, pp. 323–328 (2001)Google Scholar
  10. 10.
    Hagege, C., Roux, C.: Entre syntaxe et smantique: normalisation de la sortie de lanalyse syntaxique en vue de lamlioration de lextraction dinformation a partir de textes. In: TANL 2003, Batz-sur-Mer, France (2003)Google Scholar
  11. 11.
    Bunke, H.: Graph matching: Theoretical foundations, algorithms, and applications. In: Vision Interface 2000, pp. 82–88. Springer, Heidelberg (2000)Google Scholar
  12. 12.
    Basili, R., Zanzotto, F.M.: Parsing engineering and empirical robustness. Natural Language Engineering 8(2-3) (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Maria Teresa Pazienza
    • 1
  • Marco Pennacchiotti
    • 1
  • Fabio Massimo Zanzotto
    • 2
  1. 1.University of Roma Tor VergataRomaItaly
  2. 2.DISCoUniversity of Milano BicoccaMilanoItaly

Personalised recommendations