Abstract
This paper presents a novel feature selection algorithm for supervised verb sense disambiguation. The algorithm disambiguates and aggregates WordNet synsets of a verb's noun phrase (NP) arguments in the training data. It was then used to filter out irrelevant WordNet semantic features introduced by the ambiguity of verb NP arguments. Experimental results showed that our new feature selection method boosted our system's performance on verbs whose meanings depended heavily on their NP arguments. Furthermore, our method outperformed two standard feature selection methods, indicating its effectiveness and advantages, especially for small-sample machine learning tasks like supervised WSD.
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