Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach
<|reference_start|>Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach: In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neighboring words, morphological form, the unordered set of surrounding words, local collocations, and verb-object syntactic relation. We tested our WSD program, named {\sc Lexas}, on both a common data set used in previous work, as well as on a large sense-tagged corpus that we separately constructed. {\sc Lexas} achieves a higher accuracy on the common data set, and performs better than the most frequent heuristic on the highly ambiguous words in the large corpus tagged with the refined senses of {\sc WordNet}.<|reference_end|>
@article{ng1996integrating,
    title={Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An
  Exemplar-Based Approach},
    author={Hwee Tou Ng, Hian Beng Lee (Defence Science Organisation)},
    journal={ACL-96},
    year={1996},
    archivePrefix={arXiv},
    eprint={cmp-lg/9606032},
    primaryClass={cmp-lg cs.CL}
}