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Author(s): Miller, Tristan; Biemann, Chris; Zesch, Torsten; Gurevych, Iryna
Title: Using distributional similarity for lexical expansion in knowledge-based word sense disambiguation
In: Kay, Martin; Boitet, Christian (Hrsg.): Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), Mumbai: The COLING 2012 Organizing Committee, 2012 , S. 1781-1796
URL: http://aclweb.org/anthology-new/C/C12/C12-1109.pdf
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Computerlinguistik; Sinn; Thesaurus; Verteilung; Wort
Abstract: We explore the contribution of distributional information for purely knowledge-based word
sense disambiguation. Specifically, we use a distributional thesaurus, computed from a large
parsed corpus, for lexical expansion of context and sense information. This bridges the lexical
gap that is seen as the major obstacle for word overlap-based approaches. We apply this
mechanism to two traditional knowledge-based methods and show that distributional information
significantly improves disambiguation results across several data sets. This improvement
exceeds the state of the art for disambiguation without sense frequency information-a situation
which is especially encountered with new domains or languages for which no sense-annotated
corpus is available.
DIPF-Departments: Informationszentrum Bildung