Abstract
This paper describes a methodology for supervised word sense disambiguation that relies on standard machine learning algorithms to induce classifiers from sense-tagged training examples where the context in which ambiguous words occur are represented by simple lexical features. This constitutes a baseline approach since it produces classifiers based on easy to identify features that result in accurate disambiguation across a variety of languages. This paper reviews several systems based on this methodology that participated in the Spanish and English lexical sample tasks of the SENSEVAL-2 comparative exercise among word sense disambiguation systems. These systems fared much better than standard baselines, and were within seven to ten percentage points of accuracy of the mostly highly ranked systems.
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© 2002 Springer-Verlag Berlin Heidelberg
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Pedersen, T. (2002). A Baseline Methodology for Word Sense Disambiguation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2002. Lecture Notes in Computer Science, vol 2276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45715-1_10
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DOI: https://doi.org/10.1007/3-540-45715-1_10
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