Paper: Density Maximization in Context-Sense Metric Space for All-words WSD

ACL ID P13-1087
Title Density Maximization in Context-Sense Metric Space for All-words WSD
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

This paper proposes a novel smoothing model with a combinatorial optimization scheme for all-words word sense disam- biguation from untagged corpora. By gen- eralizing discrete senses to a continuum, we introduce a smoothing in context-sense space to cope with data-sparsity result- ing from a large variety of linguistic con- text and sense, as well as to exploit sense- interdependency among the words in the same text string. Through the smoothing, all the optimal senses are obtained at one time under maximum marginal likelihood criterion, by competitive probabilistic ker- nels made to reinforce one another among nearby words, and to suppress conflicting sense hypotheses within the same word. Experimental results confirmed the superi- ority of the proposed method over conven- tional ones by...