Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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The classification information model or CIM classifies instances by considering the discrimination ability of their features, which was proven to be useful for word sense disambiguation at Senseval-1. But the CIM has a problem of information loss. KUNLP system at Senseval-2 uses a modified version of the CIM for word sense disambiguation. We used three types of features for word sense disambiguation: local, topical, and bigram context. Local and topical context are similar to Chodorow's context and refer to only unigram information. The window of a bigram context is similar to that of a local context but a bigram context refers to only bigram information. We participated in the English lexical sample task and the Korean lexical sample task, where our systems ranked high.