Learning semantic classes for word sense disambiguation

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Learning semantic classes for word sense disambiguation (2005)

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by Upali S. Kohomban , Wee Sun Lee

Venue:	In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics
Citations:	44 - 1 self

BibTeX

@INPROCEEDINGS{Kohomban05learningsemantic,
    author = {Upali S. Kohomban and Wee Sun Lee},
    title = {Learning semantic classes for word sense disambiguation},
    booktitle = {In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics},
    year = {2005},
    pages = {34--41}
}

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Abstract

Word Sense Disambiguation suffers from a long-standing problem of knowledge acquisition bottleneck. Although state of the art supervised systems report good accuracies for selected words, they have not been shown to be promising in terms of scalability. In this paper, we present an approach for learning coarser and more general set of concepts from a sense tagged corpus, in order to alleviate the knowledge acquisition bottleneck. We show that these general concepts can be transformed to fine grained word senses using simple heuristics, and applying the technique for recent SENSEVAL data sets shows that our approach can yield state of the art performance. 1

Keyphrases

semantic class word sense disambiguation knowledge acquisition bottleneck general concept recent senseval data set simple heuristic general set system report good accuracy long-standing problem art performance word sense disambiguation suffers

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