2002/05/27 by Ted Pedersen, Pedersen, Ted
Computer Science · Decision Sciences · #AI-based Problem Solving and Planning #Bayesian Modeling and Causal Inference #Computation and Language (cs.CL) #Data Quality and Management #FOS: Computer and information sciences #I.2.7 #cs.CL
paper · pdf · doi:10.48550/arxiv.cs/0205069
Appears in the Proceedings of SENSEVAL-2: Second International Workshop on Evaluating Word Sense Disambiguation Systems July 5-6, 2001, Toulouse, France
arxiv created 2002/05/27 · openalex publication_date 2002/05/27 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.