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Composition in Distributional Models of Semantics
Author(s) -
Mitchell Jeff,
Lapata Mirella
Publication year - 2010
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/j.1551-6709.2010.01106.x
Subject(s) - natural language processing , computer science , distributional semantics , operationalization , phrase , artificial intelligence , meaning (existential) , word (group theory) , semantic similarity , similarity (geometry) , semantics (computer science) , priming (agriculture) , multiplicative function , composition (language) , linguistics , mathematics , psychology , mathematical analysis , philosophy , botany , germination , epistemology , image (mathematics) , psychotherapist , biology , programming language
Vector‐based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector‐based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in sentential priming) suggesting that semantic similarity is more complex than simply a relation between isolated words. This article proposes a framework for representing the meaning of word combinations 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 that we evaluate empirically on a phrase similarity task.