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.
['Gemma Boleda', 'Sebastian Padó', 'Jason Utt']
Regular polysemy: A distributional model