Word association norms, mutual information, and lexicography
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Conceptual analysis of lexical taxonomies: the case of WordNet top-level
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
Discovering word senses from text
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
Extracting features for verifying WordNet
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
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WordNet has become an important and useful resource for the natural language processing field. Recently, many countries have been developing their own WordNet. In this paper we show an evaluation of the Korean WordNet (U-WIN). The purpose of the work is to study how well the manually created lexical taxonomy U-WIN is built. Evaluation is done level by level, and the reason for selecting words for each level is that we want to compare each level and to find relations between them. As a result the words at a certain level (level 6) give the best score, for which we can make a conclusion that the words at this level are better organized than those at other levels. The score decreases as the level goes up or down from this particular level.