| A Workshop on Machine Learning in Natural Language Processing |
Organizers: Shalom Lapin and Ido Dagan |
Textual entailment as a framework for applied semantics
Ido Dagan
Bar-Ilan University
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 Challenges benchmarks. Then we will demonstrate directions for building up textual entailment systems and utilizing them for concrete applications. Furthermore, I will suggest that textual entailment modeling may become a comprehensive framework for applied semantics research. Such framework introduces novel useful variants for known semantic problems and also highlights important new problems which were hardly investigated so far within computational linguistics. This semantic modeling perspective will be reviewed and illustrated by a case study for an entailment variant of the word sense disambiguation problem. |