@InProceedings{silvadecarvalho-nguyen:2017:EACLlong,
  author    = {Silva de Carvalho, Danilo  and  Nguyen, Minh Le},
  title     = {Building Lexical Vector Representations from Concept Definitions},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {905--915},
  abstract  = {The use of distributional language representations have opened new paths in
	solving a variety of NLP problems. However, alternative approaches can take
	advantage of information unavailable through pure statistical means. This paper
	presents a method for building vector representations from meaning unit blocks
	called concept definitions, which are obtained by extracting information from a
	curated linguistic resource (Wiktionary). The representations obtained in this
	way can be compared through conventional cosine similarity and are also
	interpretable by humans. Evaluation was conducted in semantic similarity and
	relatedness test sets, with results indicating a performance comparable to
	other methods based on single linguistic resource extraction. The results also
	indicate noticeable performance gains when combining distributional similarity
	scores with the ones obtained using this approach. Additionally, a discussion
	on the proposed method's shortcomings is provided in the analysis of error
	cases.},
  url       = {http://www.aclweb.org/anthology/E17-1085}
}