Abstract
This paper addresses Textual Entailment (i.e. recognizing that the meaning of a text entails the meaning of another text) using a Tree Edit Distance algorithm between the syntactic trees of the two texts. A key aspect of the approach is the estimation of the cost for the editing operations (i.e. Insertion, Deletion, Substitution) among words.
The aim of the paper is to compare the contribution of two different lexical resources for recognizing textual entailment: WordNet and a word-similarity database. In both cases we derive entailment rules that are used by the Tree Edit Distance Algorithm. We carried out a number of experiments over the PASCAL-RTE dataset in order to estimate the contribution of different combinations of the available resources.
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Kouylekov, M., Magnini, B. (2006). Combining Lexical Resources with Tree Edit Distance for Recognizing Textual Entailment. 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_12
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DOI: https://doi.org/10.1007/11736790_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33427-9
Online ISBN: 978-3-540-33428-6
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