Regular polysemy: A distributional model
نویسندگان
چکیده
Many types of polysemy are not word specific, but are instances of general sense alternations such as ANIMAL-FOOD. Despite their pervasiveness, regular alternations have been mostly ignored in empirical computational semantics. This paper presents (a) a general framework which grounds sense alternations in corpus data, generalizes them above individual words, and allows the prediction of alternations for new words; and (b) a concrete unsupervised implementation of the framework, the Centroid Attribute Model. We evaluate this model against a set of 2,400 ambiguous words and demonstrate that it outperforms two baselines.
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Regular polysemy: from sense vectors to sense patterns
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