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- W142000222 abstract "We report on the use of machine learning techniques for sense disambiguation in the English all words task of SENSEVAL2. The task was to automatically assign the appropriate sense to a possibly ambiguous form given its context. A word approach was adopted, leading to a set of classifiers, each specialized in one single form-POS combination. Experts consist of multiple classifiers trained on Semcor using two types of learning techniques, viz. memory-based learning and rule-induction. Through optimization by crossvalidation of the individual classifiers and the voting scheme for combining them, the best possible expert was determined. Results show that especially memory-based learning in a word-expert approach is a feasible method for unrestricted word-sense disambiguation, even with limited training data." @default.
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- W142000222 date "2001-07-05" @default.
- W142000222 modified "2023-09-27" @default.
- W142000222 title "Classifier Optimization and Combination in the English All Words Task" @default.
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