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Learning Textual Entailment on a Distance Feature Space

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 3944)

Abstract

Textual Entailment recognition is a very difficult task as it is one of the fundamental problems in any semantic theory of natural language. As in many other NLP tasks, Machine Learning may offer important tools to better understand the problem. In this paper, we will investigate the usefulness of Machine Learning algorithms to address an apparently simple and well defined classification problem: the recognition of Textual Entailment. Due to its specificity, we propose an original feature space, the distance feature space, where we model the distance between the elements of the candidate entailment pairs. The method has been tested on the data of the Recognizing Textual Entailment (RTE) Challenge.

Keywords

  • Feature Space
  • Semantic Similarity
  • Graph Match
  • Entailment Relation
  • Common Subgraph

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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Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M. (2006). Learning Textual Entailment on a Distance Feature Space. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds) Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. MLCW 2005. Lecture Notes in Computer Science(), vol 3944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736790_14

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  • DOI: https://doi.org/10.1007/11736790_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33427-9

  • Online ISBN: 978-3-540-33428-6

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