Recognizing textual entailment using a subsequence kernel method
Rui Wang, Günter Neumann · 2007
We present a novel approach to recognizing Textual 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.