Modeling consensus: classifier combination for word sense disambiguation
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Applying alternating structure optimization to word sense disambiguation
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ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
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We propose a novel approach for improving Feature Selection for Word Sense Disambiguation by incorporating a feature relevance prior for each word indicating which features are more likely to be selected. We use transfer of knowledge from similar words to learn this prior over the features, which permits us to learn higher accuracy models, particularly for the rarer word senses. Results on the OntoNotes verb data show significant improvement over the baseline feature selection algorithm and results that are comparable to or better than other state-of-the-art methods.