Paper: Deriving Generalized Knowledge from Corpora Using WordNet Abstraction

ACL ID E09-1092
Title Deriving Generalized Knowledge from Corpora Using WordNet Abstraction
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

on Benjamin Van Durme, Phillip Michalak and Lenhart K. Schubert Department of Computer Science University of Rochester Rochester, NY 14627, USA Abstract Existing work in the extraction of com- monsense knowledge from text has been primarily restricted to factoids that serve as statements about what may possibly ob- tain in the world. We present an ap- proach to deriving stronger, more general claims by abstracting over large sets of factoids. Our goal is to coalesce the ob- served nominals for a given predicate ar- gument into a few predominant types, ob- tained as WordNet synsets. The results can be construed as generically quantified sen- tences restricting the semantic type of an argument position of a predicate.