Compositional Distributional Semantics Models in Chunk-based Smoothed Tree Kernels

The field of compositional distributional semantics has proposed very interesting and reliable models for accounting the distributional meaning of simple phrases. These models however tend to disregard the syntactic structures when they are applied to larger sentences. In this paper we propose the chunk-based smoothed tree kernels (CSTKs) as a way to exploit the syntactic structures as well as the reliability of these compositional models for simple phrases. We experiment with the recognizing textual entailment datasets. Our experiments show that our CSTKs perform better than basic compositional distributional semantic models (CDSMs) recursively applied at the sentence level, and also better than syntactic tree kernels.