Recognizing textual entailment using a subsequence kernel method

Authors:
Rui Wang;Günter Neumann
Affiliations:
LT-Lab, DFKI, Saarbrücken, Germany;LT-Lab, DFKI, Saarbrücken, Germany
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Year:
2007

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Abstract

We present a novel approach to recognizing Textual Entailment. Structural features are constructed from abstract tree descriptions, which are automatically extracted from syntactic dependency trees. These features are then applied in a subsequence-kernel-based classifier to learn whether an entailment relation holds between two texts. Our method makes use of machine learning techniques using a limited data set, no external knowledge bases (e.g. WordNet), and no handcrafted inference rules. We achieve an accuracy of 74.5% for text pairs in the Information Extraction and Question Answering task, 63.6% for the RTE-2 test data, and 66.9% for the RET-3 test data.