Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Using syntactic dependency as local context to resolve word sense ambiguity
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Learning word clusters from data types
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences
Computational Linguistics
VerbNet class assignment as a WSD task
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
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The paper describes SENSE, a word sense disambiguation system which makes use of multidimensional analogy-based proportions to infer the most likely sense of a word given its context. Architecture and functioning of the system are illustrated in detail. Results of different experimental settings are given, showing that the system, in spite its conservative bias, successfully copes with the problem of training data sparseness.