Paper: Semi-Supervised Verb Class Discovery Using Noisy Features

ACL ID W03-0410
Title Semi-Supervised Verb Class Discovery Using Noisy Features
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2003
Authors

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 s...