Paper: Learning to Merge Word Senses

ACL ID D07-1107
Title Learning to Merge Word Senses
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

It has been widely observed that different NLP appli- cations require different sense granularities in order to best exploit word sense distinctions, and that for many applications WordNet senses are too fine-grained. In contrast to previously proposed automatic methods for sense clustering, we formulate sense merging as a su- pervised learning problem, exploiting human-labeled sense clusterings as training data. We train a discrimi- native classifier over a wide variety of features derived from WordNet structure, corpus-based evidence, and evidence from other lexical resources. Our learned similarity measure outperforms previously proposed automatic methods for sense clustering on the task of predicting human sense merging judgments, yielding an absolute F-score improvement of 4.1% on nou...