Recognizing Textual Entailment Using a Subsequence Kernel Method

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.

Authors

• Rui Wang

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