This paper presents a corpus-based approach to word sense disambiguation where a decision tree assigns a sense to an ambiguous word based on the bigrams that occur nearby. This approach is evaluated using the sense-tagged corpora from the 1998 SENSEVAL word sense disambiguation exercise. It is more accurate than the average results reported for 30 of 36 words, and is more accurate than the best results for 19 of 36 words.
The decision list is a closely related approach that has also been applied to word sense disambiguation (e.g., @cite_6 , @cite_14 , @cite_4 ). Rather than building and traversing a tree to perform disambiguation, a list is employed. In the general case a decision list may suffer from less fragmentation during learning than decision trees; as a practical matter this means that the decision list is less likely to be over--trained. However, we believe that fragmentation also reflects on the feature set used for learning. Ours consists of at most approximately 100 binary features. This results in a relatively small feature space that is not as likely to suffer from fragmentation as are larger spaces.
@cite_14 @cite_4 @cite_6
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Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowledge source. We describe a system which performs unrestricted word sense disambiguation (on all content words in free text) by combining different knowledge sources: semantic preferences, dictionary definitions and subject domain codes along with part-of-speech tags. The usefulness of these sources is optimised by means of a learning algorithm. We also describe the creation of a new sense tagged corpus by combining existing resources. Tested accuracy of our approach on this corpus exceeds 92 , demonstrating the viability of all-word disambiguation rather than restricting oneself to a small sample. This paper describes a supervised algorithm for word sensedisambiguation based on hierarchies of decision lists. This algorithmsupports a useful degree of conditional branching while minimizing thetraining data fragmentation typical of decision trees. Classificationsare based on a rich set of collocational, morphological and syntacticcontextual features, extracted automatically from training data andweighted sensitive to the nature of the feature and feature class. Thealgorithm is evaluated comprehensively in the SENSEVAL framework,achieving the top performance of all participating supervised systems onthe 36 test words where training data is available.