Paper: Automating Feature Set Selection For Case-Based Learning Of Linguistic Knowledge

ACL ID W96-0211
Title Automating Feature Set Selection For Case-Based Learning Of Linguistic Knowledge
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 1996
Authors

This paper addresses the issue of "algorithm vs. representation" for case-based learning of lin- guistic knowledge. We first present empirical evidence that the success of case-based learning methods for natural language processing tasks de- pends to a large degree on the feature set used to describe the training instances. Next, we present a technique for automating feature set selection for case-based learning of linguistic knowledge. Given as input a baseline case representation, the method modifies the representation in response to a number of predefined linguistic biases by adding, deleting, and weighting features appro- priately. We apply the linguistic bias approach to feature set selection to the problem of relative pronoun disambiguation and show that the case- based learning algo...