@InProceedings{gamallo:2017:RepL4NLP,
  author    = {Gamallo, Pablo},
  title     = {Sense Contextualization in a Dependency-Based Compositional Distributional Model},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1--9},
  abstract  = {Little attention has been paid to distributional compositional methods which
	employ syntactically structured vector models. As word vectors belonging to
	different syntactic categories                                      have incompatible
	syntactic
	distributions, no
	trivial compositional operation can be applied to combine them into a new
	compositional vector. 
	In this article, we generalize the method described by                               
	     
	Erk
	and
	Pad\'{o}
	(2009) by
	proposing a dependency-base framework that contextualize not only lemmas but
	also selectional preferences.  The main contribution of the article is to
	expand their model to a fully compositional framework in which syntactic
	dependencies are put at the core of semantic composition.
	 We claim that semantic composition is mainly driven by syntactic dependencies.
	Each syntactic dependency generates two new compositional vectors representing
	the contextualized sense of the two related lemmas.  The sequential 
	application of the compositional operations associated to the dependencies
	results in as many contextualized vectors as lemmas the composite expression
	contains. At the end of the semantic process, we do not obtain a single
	compositional vector representing the semantic denotation of the whole
	composite expression, but one contextualized vector for each lemma of the whole
	expression. Our method avoids the troublesome high-order tensor representations
	by defining lemmas and selectional restrictions as first-order tensors (i.e.
	standard vectors). 
	A corpus-based experiment is performed to both evaluate the quality of the
	compositional vectors built with our strategy, and to compare them to other
	approaches on distributional compositional semantics. The experiments show that
	our dependency-based compositional method performs as  (or even better than)
	the state-of-the-art.},
  url       = {http://www.aclweb.org/anthology/W17-2601}
}