Vector-space models derived from corpora are an effective way to learn a representation of word meaning directly from data, and these models have many uses in practical applications. A number of unsu-pervised approaches have been proposed to automatically learn representations of word senses directly from corpora, but since these methods use no information but the words themselves, they sometimes miss distinctions that could be possible to make if more information were available. In this paper, we present a general framework that we call context enrichment that incorporates external information during the training of multi-sense vector-space models. Our approach is agnostic as to which external signal is used to enrich the context, but in this work we consider the use of translations as the source of enrichment. We evaluated the models trained using the translation-enriched context using several similarity benchmarks and a word analogy test set. In all our evaluations, the enriched model outperformed the purely word-based baseline soundly.
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