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Academic Paper

Title: A machine learning approach to textual entailment recognition
Author: Fabio Massimo Zanzotto
Institution: Università degli Studi di Roma - La Sapienza
Author: Marco Pennacchiotti
Institution: Yahoo! Research
Author: Alessandro Moschitti
Institution: Università degli Studi di Trento
Linguistic Field: Computational Linguistics; Pragmatics; Semantics; Syntax
Abstract: Designing models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so.


This article appears IN Natural Language Engineering Vol. 15, Issue 4, which you can READ on Cambridge's site or on LINGUIST .

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