Using multiple knowledge sources for word sense discrimination
Computational Linguistics
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The interaction of knowledge sources in word sense disambiguation
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WorkSense '00 Proceedings of the ACL-2000 Workshop on Word Senses and Multi-Linguality
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Expert Systems with Applications: An International Journal
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Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowledge source. We describe a system which performs word sense disambiguation on all content words in free text by combining different knowledge sources: semantic preferences, dictionary definitions and subject/domain codes along with part-of-speech tags, optimised by means of a learning algorithm. We also describe the creation of a new sense tagged corpus by combining existing resources. Tested accuracy of our approach on this corpus exceeds 92%, demonstrating the viability of all-word disambiguation rather than restricting oneself to a small sample.