From the view point of the linguistic typology, Korean and Japanese have many grammatical similarities which enable it to easily construct a sense-tagged Korean corpus through an existing high-quality Japanese-to-Korean machine translation system. The sense-tagged corpus may serve as a knowledge source to extract useful clues for word sense disambiguation (WSD). This paper addresses a disambiguation model for Korean nouns, whose execution is based on the concept codes extracted from the

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... gged corpus and the semantic similarity values over a thesaurus hierarchy. By the help of the automatically constructed sensetagged corpus, we overcome the knowledge acquisition bottleneck. Also, we show that the performance of word sense disambiguation can be improved by combining several base classifiers. In an experimental evaluation, the proposed model using a majority voting achieved an average precision of 77.75% with an improvement over the baseline by 15.00%, which is very promising for real world MT systems.