Semantic inference at the lexical-syntactic level for textual entailment recognition

R Bar-Haim, I Dagan, I Greental, I Szpektor, M Friedman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageAmerican English
Title of host publicationACL-PASCAL Workshop on Textual Entailment and Paraphrasing
PublisherAssociation for Computational Linguistics
StatePublished - 2007

Bibliographical note

Place of conference:Prague, Czech Republic


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