@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}
}