Vector-based Models of Semantic Composition

Jeff Mitchell, Mirella Lapata

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


This paper proposes a framework for representing the meaning of phrases and sentences in vector space. Central to our approach is vector composition which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models which we evaluate empirically on a sentence similarity task. Experimental results demonstrate that the multiplicative models are superior to the additive alternatives when compared against human judgments.
Original languageEnglish
Title of host publicationProceedings of ACL-08: HLT
Number of pages9
Publication statusPublished - 2008


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