@InProceedings{perez-casillas-gojenola:2016:BioTxtM2016,
  author    = {P\'{e}rez, Alicia  and  Casillas, Arantza  and  Gojenola, Koldo},
  title     = {Fully unsupervised low-dimensional representation of adverse drug reaction events through distributional semantics},
  booktitle = {Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {50--59},
  abstract  = {Electronic health records show great variability since the same concept is
	often expressed with different terms,  either scientific  latin forms, common
	or lay variants and even vernacular naming. Deep learning enables 
	distributional representation of  terms in a vector-space, and therefore,
	related terms tend to be close in the vector space. Accordingly, embedding
	words through these vectors opens the way towards accounting for semantic
	relatedness through classical algebraic operations.  
	In this work we propose a simple though efficient unsupervised characterization
	of Adverse Drug Reactions (ADRs). This approach exploits the embedding
	representation of the terms involved in candidate ADR events, that is,
	drug-disease entity pairs. In brief, the ADRs are represented as vectors that
	link the drug  with the disease in their context through a recursive additive
	model.
	We discovered that a low-dimensional representation that makes use of the
	modulus and argument of the embedded representation of the ADR event shows 
	correlation with the manually annotated class. Thus, it can be derived that
	this characterization results in to be beneficial  for further classification
	tasks as predictive features.},
  url       = {http://aclweb.org/anthology/W16-5106}
}