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

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

2002-05-27

arxiv.org/abs/cs/0205069v1

Computer Science

Computation and Language

Appears in the Proceedings of SENSEVAL-2: Second International Workshop on Evaluating Word Sense Disambiguation Systems July 5

Scientific paper

This 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.

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