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Using Distributional Similarity for Lexical Expansion in Knowledge-based Word Sense Disambiguation

Miller, Tristan and Biemann, Chris and Zesch, Torsten and Gurevych, Iryna (2012):
Using Distributional Similarity for Lexical Expansion in Knowledge-based Word Sense Disambiguation.
In: Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), Mumbai, India, pp. 1781-1796, [Online-Edition: http://aclweb.org/anthology/C/C12/C12-1109.pdf],
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Miller, Tristan and Biemann, Chris and Zesch, Torsten and Gurevych, Iryna
Title: Using Distributional Similarity for Lexical Expansion in Knowledge-based Word Sense Disambiguation
Language: English
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.

Title of Book: Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012)
Uncontrolled Keywords: reviewed;UKP_p_SIDIM;Statistical Semantics;word sense disambiguation, distributional thesaurus, lexical expansion
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Sprachtechnologie
20 Department of Computer Science > Ubiquitous Knowledge Processing
Event Location: Mumbai, India
Date Deposited: 31 Dec 2016 14:29
Official URL: http://aclweb.org/anthology/C/C12/C12-1109.pdf
Identification Number: TUD-CS-2012-0232
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