Semantic inference at the lexical-syntactic level for textual entailment recognition
Roy Bar Haim, Ido Dagan, Iddo Greental, Idan Szpektor and Moshe Friedman
In: ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, 28-29 June 2007, Prague, Czech Republic.
We present a new framework for textual entailment, which provides a modular integration between knowledge-based exact inference and cost-based approximate matching. Diverse types of knowledge are uniformly represented as entailment rules, which were acquired both manually and automatically. Our proof system operates directly on parse
trees, and infers new trees by applying entailment rules, aiming to strictly generate the target hypothesis from the source text. In order to cope with inevitable knowledge gaps,
a cost function is used to measure the remaining “distance” from the hypothesis.