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

Proceedings of the Annual Meeting of the Association for Computational Linguistics

Volumn , Issue , 2001, Pages 139-142

  Pedersen, Ted ^a  

^a UNIVERSITY OF MINNESOTA DULUTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; MACHINE LEARNING; NATURAL LANGUAGE PROCESSING SYSTEMS;

HIGHLY-PORTABLE; LEXICAL FEATURES; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING; ROBUST METHODS; SIMPLE++; STANDARD MACHINES; WORD SENSE DISAMBIGUATION SYSTEMS;

LEARNING ALGORITHMS;

EID: 85149133535     PISSN: 0736587X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (2)

1. T. Pedersen. 2000. A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation. In Proceedings of the First Annual Meeting of the North American Chapter of the Association for Computational Linguistics, pages 63-69, Seattle, WA, May.
2. T. Pedersen. 2001. A decision tree of bigrams is an accurate predictor of word sense. In Proceedings of the Second Annual Meeting of the North American Chapter of the Association -for: Computational Linguistics, pages 79-86, Pittsburgh, July.