Textual entailment as a framework for applied semantics


March 29, 2007


Ido Dagan


Bar Ilan University, Israel


We have recently proposed Recognizing Textual Entailment (RTE) as a generic task that captures major semantic inferences across different natural language processing applications. The talk will first review the motivation and definition of the textual entailment task and the PASCAL RTE-1,2&3 Challenges benchmarks. Then we will demonstrate directions for building textual entailment systems, based on knowledge acquisition and inference, and for utilizing them within concrete applications. Furthermore, we suggest that textual entailment modeling may become a comprehensive framework for applied semantics research. Such framework introduces useful variants of known semantic problems and highlights important tasks which were hardly investigated so far at an applied computational level. The semantic modeling perspective will be illustrated in more detail by a case study for an entailment-based variant of word sense disambiguation.


Ido Dagan

Ido Dagan is a Senior Lecturer at the Department of Computer Science at Bar Ilan University, Israel. His areas of interest are largely within empirical NLP, particularly empirical approaches for applied semantic processing. In the last few years Ido and his colleagues introduced textual entailment as a generic framework for applied semantic inference and have organized the first three rounds of the PASCAL Recognizing Textual Entailment Challenges. Ido received his Ph.D. from the Technion. He has been a research fellow at the IBM Haifa Scientific Center and a Member of Technical Staff at AT&T Bell Laboratories. During 1998-2003 he was co-founder and CTO of FocusEngine and VP of Technology of LingoMotors.