Semisupervised Verb Class Discovery Using Noisy Features

We cluster verbs into lexical semantic classes, using a general set of noisy features that cap-ture syntactic and semantic properties of the verbs. The feature set was previously shown to work well in a supervised learning setting, us-ing known English verb classes. In moving to a scenario of verb class discovery, using cluster-ing, we face the problem of having a large num-ber of irrelevant features for a particular cluster-ing task. We investigate various approaches to feature selection, using both unsupervised and semi-supervised methods, comparing the results to subsets of features manually chosen accord-ing to linguistic properties. We find that the un-supervised method we tried cannot be consis-tently applied to our data. However, the semi-supervised approach (using a seed set of sam-ple verbs) overall outperforms not only the full set of features, but the hand-selected features as well.