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.
REF propose a framework to represent the meaning of the combination p + a as a function f operating on four components:
Vector-based Models of Semantic Composition
Annual Meeting Of The Association For Computational Linguistics
Computer Science