Extracting features for verifying WordNet

Authors:
Altangerel Chagnaa;Cheol-Young Ock;Ho-Seop Choe
Affiliations:
School of Computer Engineering and Information Technology, University of Ulsan, South Korea;School of Computer Engineering and Information Technology, University of Ulsan, South Korea;Information System Development Team, Korean Institute of Science and Technology Information, Daejeon, South Korea
Venue:
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Year:
2007

Citing 3
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Word association norms, mutual information, and lexicography

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On the evaluation of Korean wordnet

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Abstract

WordNet is a semantic lexicon for the English language and many countries have been developing their own WordNet. Almost, all of the WordNets are manually built and unfortunately these WordNets are not verified and are being used in many knowledge-based applications. In this paper we aimed at the clustering based verification of a manually built lexical taxonomy WordNet, namely the Korean WordNet, U-WIN. For this purpose two kinds of clustering methods are used: K-Means approach and ICA based approach. As a result the ICA based approach gives better result, and it shows very effective characteristic for extracting features.