Generalizing biomedical relation classification with neural adversarial domain adaptation.

ABSTRACT:

Motivation

Creating large datasets for biomedical relation classification can be prohibitively expensive. While some datasets have been curated to extract protein-protein and drug-drug interactions (PPIs and DDIs) from text, we are also interested in other interactions including gene-disease and chemical-protein connections. Also, many biomedical researchers have begun to explore ternary relationships. Even when annotated data are available, many datasets used for relation classification are inherently biased. For example, issues such as sample selection bias typically prevent models from generalizing in the wild. To address the problem of cross-corpora generalization, we present a novel adversarial learning algorithm for unsupervised domain adaptation tasks where no labeled data a

SUBMITTER: Rios A 

PROVIDER: S-EPMC6129312 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature