Topic Models for Meaning Similarity in Context

Georgiana Dinu, Mirella Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recent work on distributional methods for similarity focuses on using the context in which a target word occurs to derive context-sensitive similarity computations. In this paper we present a method for computing similarity which builds vector representations for words in context by modelling senses as latent variables in a large corpus. We apply this to the Lexical Substitution Task and we show that our model significantly outperforms typical distributional methods.
Original languageEnglish
Title of host publicationCOLING 2010, 23rd International Conference on Computational Linguistics, Posters Volume, 23-27 August 2010, Beijing, China
PublisherAssociation for Computational Linguistics
Number of pages9
Publication statusPublished - 2010


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