@InProceedings{legrand-EtAl:2018:LOUHI,
  author    = {Legrand, Joël  and  Toussaint, Yannick  and  Raïssi, Chedy  and  Coulet, Adrien},
  title     = {Syntax-based Transfer Learning for the Task of Biomedical Relation Extraction},
  booktitle = {Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {149--159},
  abstract  = {Transfer learning (TL) proposes to enhance machine learning performance on a problem, by reusing labeled data originally designed for a related problem. In particular, domain adaptation consists, for a specific task, in reusing training data developed for the same task but a distinct domain. This is particularly relevant to the applications of deep learning in Natural Language Processing, because those usually require large annotated corpora that may not exist for the targeted domain, but exist for side domains.},
  url       = {http://www.aclweb.org/anthology/W18-5617}
}