Paper: Learning A Robust Word Sense Disambiguation Model Using Hypernyms In Definition Sentences

ACL ID C04-1132
Title Learning A Robust Word Sense Disambiguation Model Using Hypernyms In Definition Sentences
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004
Authors

This paper proposes a method to improve the robustness of a word sense disambigua- tion (WSD) system for Japanese. Two WSD classifiers are trained from a word sense-tagged corpus: one is a classifier ob- tained by supervised learning, the other is a classifier using hypernyms extracted from definition sentences in a dictionary. The for- mer will be suitable for the disambiguation of high frequency words, while the latter is appropriate for low frequency words. A ro- bust WSD system will be constructed by combining these two classifiers. In our ex- periments, the F-measure and applicability of our proposed method were 3.4% and 10% greater, respectively, compared with a single classifier obtained by supervised learning.