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
