abstract
deep compositional models of meaning
acting on distributional representations of
words in order to produce vectors of larger
text constituents are evolving to a popular area of nlp research. we detail
a compositional distributional framework
based on a rich form of word embeddings
that aims at facilitating the interactions
between words in the context of a sentence. embeddings and composition layers are jointly learned against a generic
objective that enhances the vectors with
syntactic information from the surrounding context. furthermore, each word is
associated with a number of senses, the
most plausible of which is selected dynamically during the composition process.
we evaluate the produced vectors qualitatively and quantitatively with positive results. at the sentence level, the effectiveness of the framework is demonstrated on
the msrpar task, for which we report results within the state-of-the-art range.