This paper presents the results of a set of methods to cluster WordNet word senses. The methods rely on different information sources: confusion matrixes from Senseval-2 Word Sense Disambiguation systems, translation similarities, hand-tagged examples of the target word senses and examples obtained automatically from the web for the target word senses. The clustering results have been evaluated using the coarsegrained word senses provided for the lexical sample in Senseval-2. We have used Cluto, a general clustering environment, in order to test different clustering algorithms. The best results are obtained for the automatically obtained examples, yielding purity values up to 84% on average over 20 nouns.
REF analyze a set of methods to cluster WordNet senses based on the use of confusion matrices from the results of WSD systems, translation equivalences, and topic signatures (word co-occurrences extracted from the web).
Clustering WordNet word senses
RANLP
Computer Science