Towards Syntax-aware Compositional Distributional Semantic Models

Compositional Distributional Semantics Models (CDSMs) are traditionally seen as an entire different world with respect to Tree Kernels (TKs). In this paper, we show that under a suitable regime these two approaches can be regarded as the same and, thus, structural information and distributional semantics can successfully cooperate in CSDMs for NLP tasks. Leveraging on distributed trees, we present a novel class of CDSMs that encode both structure and distributional meaning: the distributed smoothed trees (DSTs). By using DSTs to compute the similarity among sentences, we implicitly define the distributed smoothed tree kernels (DSTKs). Experiment with our DSTs show that DSTKs approximate the corresponding smoothed tree kernels (STKs). Thus, DSTs encode both structural and distributional semantics of text fragments as STKs do. Experiments on RTE and STS show that distributional semantics encoded in DSTKs increase performance over structure-only kernels.