It has been widely observed that different NLP applications 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 supervised learning problem, exploiting human-labeled sense clusterings as training data. We train a discriminative 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 nouns, 13.6% on verbs, and 4.0% on adjectives. Finally, we propose a model for clustering sense taxonomies using the outputs of our classifier, and we make available several automatically sense-clustered WordNets of various sense granularities.
REF presented a novel supervised approach in which they train a Support Vector Machine(SVM) using features derived from WordNet and other lexical resources, whose predictions serve as a distance measure between synsets.
Learning to Merge Word Senses
2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)
Computer Science