Syntax-based transfer learning for the task of biomedical relation extraction.

Oncology/Hematology State Required CME
Journal: Journal of biomedical semantics
Published Date:

Abstract

BACKGROUND: Transfer learning aims at enhancing machine learning performance on a problem by reusing labeled data originally designed for a related, but distinct problem. In particular, domain adaptation consists for a specific task, in reusing training data developedfor the same task but a distinct domain. This is particularly relevant to the applications of deep learning in Natural Language Processing, because they usually require large annotated corpora that may not exist for the targeted domain, but exist for side domains.

Authors

  • Joël Legrand
    Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.
  • Yannick Toussaint
    Université de Lorraine, CNRS, Inria, LORIA, Nancy, France.
  • Chedy Raïssi
    Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.
  • Adrien Coulet
    LORIA (CNRS, Inria Nancy-Grand Est, University of Lorraine), Campus Scientifique, Nancy, France. [email protected].