Paper: Automatic Selection Of Class Labels From A Thesaurus For An Effective Semantic Tagging Of Corpora

ACL ID A97-1055
Title Automatic Selection Of Class Labels From A Thesaurus For An Effective Semantic Tagging Of Corpora
Venue Applied Natural Language Processing Conference
Session Main Conference
Year 1997
Authors

It is widely accepted that tagging text with se- mantic information would improve the quality of lexical learning in corpus-based NLP methods. However available on-line taxonomies are rather entangled and introduce an unnecessary level of ambiguity. The noise produced by the re- dundant number of tags often overrides the ad- vantage of semantic tagging. In this paper we propose an automatic method to select from WordNet a subset of domain-appropriate cate- gories that effectively reduce the overambiguity of WordNet, and help at identifying and cate- gorise relevant language patterns in a more com- pact way. The method is evaluated against a manually tagged corpus, SEMCOR.