A major focus of current work in distributional models of semantics is to construct phrase representations compositionally from word representations. However, the syntactic contexts which are modelled are usually severely limited, a fact which is reflected in the lexical-level WSD-like evaluation methods used. In this paper, we broaden the scope of these models to build sentence-level representations, and argue that phrase representations are best evaluated in terms of the inference decisions that they support, invariant to the particular syntactic constructions used to guide composition. We propose two evaluation methods in relation classification and QA which reflect these goals, and apply several recent compositional distributional models to the tasks. We find that the models outperform a simple lemma overlap baseline slightly, demonstrating that distributional approaches can already be useful for tasks requiring deeper inference. 1

Abstract not found

Single-word vector space models have been very successful at learning lexical information. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length. Our model assigns a vector and a matrix to every node in a parse tree: the vector captures the inherent meaning of the constituent, while the matrix captures how it changes the meaning of neighboring words or phrases. This matrix-vector RNN can learn the meaning of operators in propositional logic and natural language. The model obtains state of the art performance on three different experiments: predicting fine-grained sentiment distributions of adverb-adjective pairs; classifying sentiment labels of movie reviews and classifying semantic relationships such as cause-effect or topic-message between nouns using the syntactic path between them. 1