Paper: Broad-Coverage Sense Disambiguation And Information Extraction With A Supersense Sequence Tagger

ACL ID W06-1670
Title Broad-Coverage Sense Disambiguation And Information Extraction With A Supersense Sequence Tagger
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

In this paper we approach word sense disambiguation and information extrac- tion as a unified tagging problem. The task consists of annotating text with the tagset defined by the 41 Wordnet super- sense classes for nouns and verbs. Since the tagset is directly related to Wordnet synsets, the tagger returns partial word sense disambiguation. Furthermore, since the noun tags include the standard named entity detection classes – person, location, organization, time, etc. – the tagger, as a by-product, returns extended named en- tity information. We cast the problem of supersense tagging as a sequential label- ing task and investigate it empirically with a discriminatively-trained Hidden Markov Model. Experimental evaluation on the main sense-annotated datasets available, i.e., Semcor and ...