A Supervised Korean Verb Sense Disambiguation Algorithm Based on Decision Lists of Syntactic Features
We present a new approach for resolving sense ambiguity using the decision lists of syntactic features. This approach exploits the 25 syntactic features including the basic lexical features in the target verb and surrounding words. Our word sense disambiguation algorithm selects the correct sense by utilizing the strongest evidence on the decision lists when the evidence is ranked at the higher level of the decision lists. If the strongest one is not available the contributions of all features that provide weak evidence are summed up and taken into account for the selection. The experiments with ten Korean ambiguous verbs show significant improvement of performance than the decision lists algorithm. In addition, results of experiments show that the syntactic features provide more significant evidences than unordered surrounding words for resolving sense ambiguity.
KeywordsTarget Word Ambiguous Word Training Corpus Word Sense Disambiguation Computational Linguistics
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