| Summary: | This paper describes a supervised algorithm for word sense disambiguation based on hierarchies of decision lists. This algorithm supports a useful degree of conditional branching while minimizing the training data fragmentation typical of decision trees. Classifications are based on a rich set of collocational, morphological and syntactic contextual features, extracted automatically from training data and weighted sensitive to the nature of the feature and feature class. The algorithm is evaluated comprehensively in the SENSEVAL framework, achieving the top performance of all participating supervised systems on the 36 test words where training data is available.
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