Modeling Word Meaning in Context with Substitute Vectors

By Oren Melamud, Ido Dagan, Jacob Goldberger. 2015

50 citations recorded by the source corpus.

Context representations are a key element in distributional models of word meaning. In contrast to typical representations based on neighboring words, a recently proposed approach suggests to represent a context of a target word by a substitute vector, comprising the potential fillers for the target word slot in that context. In this work we first propose a variant of substitute vectors, which we find particularly suitable for measuring context similarity. Then, we propose a novel model for representing word meaning in context based on this context representation. Our model outperforms state-of-the-art results on lexical substitution tasks in an unsupervised setting.

Subfields: Artificial Intelligence, Natural Language Processing, Representation & Self-Supervised Learning

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Citations in the corpus

context2vec: Learning Generic Context Embedding with Bidirectional LSTM (2016, methodology): “The second-best is due to Melamud et al. (2015b).”