Paper: Learning Semantic Classes For Word Sense Disambiguation

ACL ID P05-1005
Title Learning Semantic Classes For Word Sense Disambiguation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005

Word Sense Disambiguation suffers from a long-standing problem of knowledge ac- quisition bottleneck. Although state of the art supervised systems report good accu- racies for selected words, they have not been shown to be promising in terms of scalability. In this paper, we present an ap- proach for learning coarser and more gen- eral set of concepts from a sense tagged corpus, in order to alleviate the knowl- edge acquisition bottleneck. We show that these general concepts can be transformed to fine grained word senses using simple heuristics, and applying the technique for recent SENSEVAL data sets shows that our approach can yield state of the art perfor- mance.