@InProceedings{ferre-zweigenbaum-nedellec:2017:BioNLP17,
  author    = {Ferr\'{e}, Arnaud  and  Zweigenbaum, Pierre  and  N\'{e}dellec, Claire},
  title     = {Representation of complex terms in a vector space structured by an ontology for a normalization task},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {99--106},
  abstract  = {We propose in this paper a semi-supervised method for labeling terms of texts
	with concepts of a domain ontology. The method generates continuous vector
	representations of complex terms in a semantic space structured by the
	ontology. The proposed method relies on a distributional semantics approach,
	which generates initial vectors for each of the extracted terms. Then these
	vectors are embedded in the vector space constructed from the structure of the
	ontology. This embedding is carried out by training a linear model. Finally, we
	apply a distance calculation to determine the proximity between vectors of
	terms and vectors of concepts and thus to assign ontology labels to terms. We
	have evaluated the quality of these representations for a normalization task by
	using the concepts of an ontology as semantic labels. Normalization of terms is
	an important step to extract a part of the information containing in texts, but
	the vector space generated might find other applications. The performance of
	this method is comparable to that of the state of the art for this task of
	standardization, opening up encouraging prospects.},
  url       = {http://www.aclweb.org/anthology/W17-2312}
}