Recognizing Textual Entailment Using a Subsequence Kernel Method

We present a novel approach to recognizing Textual nt. 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.

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Rui Wang 0005, Günter Neumann
• Keywords: AAAI 2007, Abstract Tree Descriptions, Intelligent Agents, Syntactic Dependency Trees, Test Data