Word sense disambiguation using a second language monolingual corpus
Computational Linguistics
Towards building contextual representations of word senses using statistical models
Corpus processing for lexical acquisition
Topical clustering of MRD senses based on information retrieval techniques
Computational Linguistics - Special issue on word sense disambiguation
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Statistical sense disambiguation with relatively small corpora using dictionary definitions
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
HLT '91 Proceedings of the workshop on Speech and Natural Language
Taxonomy and Lexical Semantics - From the Perspective of Machine Readable Dictionaries
AMTA '98 Proceedings of the Third Conference of the Association for Machine Translation in the Americas on Machine Translation and the Information Soup
Word sense disambiguation using static and dynamic sense vectors
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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Word sense disambiguation for unrestricted text is one of the most difficult tasks in the fields of computational linguistics. The crux of the problem is to discover a model that relates the intended sense of a word with its context. This paper describes a general framework for adaptive conceptual word sense disambiguation. Central to this WSD framework is the sense division and semantic relations based on topical analysis of dictionary sense definitions. The process begins with an initial disambiguation step using an MRD-derived knowledge base. An adaptation step follows to combine the initial knowledge base with knowledge gleaned from the partial disambiguated text. Once the knowledge base is adjusted to suit the text at hand, it is then applied to the text again to finalize the disambiguation result. Definitions and example sentences from LDOCE are employed as training materials for WSD, while passages from the Brown corpus and Wall Street Journal are used for testing. We report on several experiments illustrating effectiveness of the adaptive approach.