We cluster verbs into lexical semantic classes, using a general set of noisy features that capture syntactic and semantic properties of the verbs. The feature set was previously shown to work well in a supervised learning setting, using known English verb classes. In moving to a scenario of verb class discovery, using clustering, we face the problem of having a large number of irrelevant features for a particular clustering task. We investigate various approaches to feature selection, using both unsupervised and semi-supervised methods, comparing the results to subsets of features manually chosen according to linguistic properties. We find that the unsupervised method we tried cannot be consistently applied to our data. However, the semisupervised approach (using a seed set of sample verbs) overall outperforms not only the full set of features, but the hand-selected features as well.
REF applied semi-supervised learning for verb class discovery.
Semi-Supervised Verb Class Discovery Using Noisy Features
Conference On Computational Natural Language Learning CoNLL
Computer Science