Textual Entailment Recognition Based on Dependency Analysis and WordNet

  • Jesús Herrera
  • Anselmo Peñas
  • Felisa Verdejo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3944)


The Recognizing Textual Entailment System shown here is based on the use of a broad-coverage parser to extract dependency relationships; in addition, WordNet relations are used to recognize entailment at the lexical level. The work investigates whether the mapping of dependency trees from text and hypothesis give better evidence of entailment than the matching of plain text alone. While the use of WordNet seems to improve system’s performance, the notion of mapping between trees here explored (inclusion) shows no improvement, suggesting that other notions of tree mappings should be explored such as tree edit distances or tree alignment distances.


Dependency Analysis Training Corpus Dependency Tree Entailment Relation Lexical Unit 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barzilay, R., Lee, L.: Learning to Paraphrase: An Unsupervised Approach UsingMultiple- Sequence Alignment. In: NAACL-HLT (2003)Google Scholar
  2. 2.
    Bille, P.: Tree Edit Distance, Alignment Distance and Inclusion. Technical Report TR-2003- 23, IT Technical Report Series (March 2003)Google Scholar
  3. 3.
    Dagan, I., Glickman, O., Magnini, B.: The PASCAL Recognising Textual Entailment Challenge. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment, Southampton, UK, April 2005, pp. 1–8 (2005)Google Scholar
  4. 4.
    Gusfield, R.: Algoritms on Strings, Trees and Sequences. Cambridge University Press, Cambridge (1997)CrossRefMATHGoogle Scholar
  5. 5.
    Hermjakob, U., Echibabi, A., Marcu, D.: Natural Language Based Reformulation Resource and Web Exploitation for Question Answering. In: Proceedings of TREC (2002)Google Scholar
  6. 6.
    Kilpeläinen, P.: Tree Matching Problems with Applications to Structured Text Databases. Technical Report A-1992-6, Department of Computer Science, University of Helsinki, Helsinki, Finland (November 1992)Google Scholar
  7. 7.
    Kouylekov, M., Magnini, B.: Recognizing Textual Entailment with Tree Edit Distance Algorithms. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment, Southampton, UK, April 2005, pp. 17–20 (2005)Google Scholar
  8. 8.
    Levensthein, V.I.: Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics - Doklady 10, 707–710 (1966)MathSciNetGoogle Scholar
  9. 9.
    Lin, D.: Dependency-based Evaluation of MINIPAR. In: Workshop on the Evaluation of Parsing Systems, Granada, Spain (May 1998)Google Scholar
  10. 10.
    Lin, D., Pantel, P.: DIRT - Discovery of Inference Rules from Text. In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 323–328 (2001)Google Scholar
  11. 11.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufman, San Francisco (1993)Google Scholar
  12. 12.
    Szpektor, I., Tanev, H., Dagan, I., Coppola, B.: Scaling Web-Based Acquisition of Entailment Relations. In: Proceedings of Empirical Methods in Natural Language Processing, EMNLP 2004 (2004)Google Scholar
  13. 13.
    Valiente, G.: An Efficient Bottom-Up Distance Between Trees. In: Proceedings of the International Symposium on String Processing and Information REtrieval, SPIRE, pp. 212–219 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jesús Herrera
    • 1
  • Anselmo Peñas
    • 1
  • Felisa Verdejo
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad Nacional de Educación a DistanciaMadridSpain

Personalised recommendations