Paper: Syntactic/Semantic Structures for Textual Entailment Recognition

ACL ID N10-1146
Title Syntactic/Semantic Structures for Textual Entailment Recognition
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

In this paper, we describe an approach based on off-the-shelf parsers and semantic re- sources for the Recognizing Textual Entail- ment (RTE) challenge that can be generally applied to any domain. Syntax is exploited by means of tree kernels whereas lexical se- mantics is derived from heterogeneous re- sources, e.g. WordNet or distributional se- mantics through Wikipedia. The joint syn- tactic/semantic model is realized by means of tree kernels, which can exploit lexical related- ness to match syntactically similar structures, i.e. whose lexical compounds are related. The comparative experiments across different RTE challenges and traditional systems show that our approach consistently and meaningfully achieves high accuracy, without requiring any adaptation or tuning.