KUNLP system using Classification Information Model at SENSEVAL-2
Hee-Cheol Seo, Sang-Zoo Lee, Hae‐Chang Rim, Kyoung Ho Lee · 2001
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. 1