Machine Learning with Lexical Features: The Duluth Approach to Senseval-2

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dc.creatorPedersen, Ted
dc.date2002-05-27
dc.date.accessioned2026-07-07T03:18:27Z
dc.date.available2026-07-07T03:18:27Z
dc.descriptionThis paper describes the sixteen Duluth entries in the Senseval-2 comparative exercise among word sense disambiguation systems. There were eight pairs of Duluth systems entered in the Spanish and English lexical sample tasks. These are all based on standard machine learning algorithms that induce classifiers from sense-tagged training text where the context in which ambiguous words occur are represented by simple lexical features. These are highly portable, robust methods that can serve as a foundation for more tailored approaches.
dc.descriptionAppears in the Proceedings of SENSEVAL-2: Second International Workshop on Evaluating Word Sense Disambiguation Systems July 5-6, 2001, Toulouse, France
dc.identifierhttps://arxiv.org/abs/cs/0205069
dc.identifierhttp://arxiv.org/abs/cs/0205069
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31120
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleMachine Learning with Lexical Features: The Duluth Approach to Senseval-2
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