Recognizing textual entailment: Rational, evaluation and approaches

Ido Dagan, Bill Dolan, Bernardo Magnini, Dan Roth

Research output: Contribution to journalReview article

Abstract

The goal of identifying textual entailment whether one piece of text can be plausibly inferred from another has emerged in recent years as a generic core problem in natural language understanding. Work in this area has been largely driven by the PASCAL Recognizing Textual Entailment (RTE) challenges, which are a series of annual competitive meetings. The current work exhibits strong ties to some earlier lines of research, particularly automatic acquisition of paraphrases and lexical semantic relationships and unsupervised inference in applications such as question answering, information extraction and summarization. It has also opened the way to newer lines of research on more involved inference methods, on knowledge representations needed to support this natural language understanding challenge and on the use of learning methods in this context. RTE has fostered an active and growing community of researchers focused on the problem of applied entailment. This special issue of the JNLE provides an opportunity to showcase some of the most important work in this emerging area.

Original languageEnglish (US)
Pages (from-to)i-xvii
JournalNatural Language Engineering
Volume15
Issue number4
DOIs
StatePublished - Oct 2009

ASJC Scopus subject areas

  • Software
  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

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