Identifying adverse drug reaction entities from social media with adversarial transfer learning model

Abstract

Identifying adverse drug reaction (ADR) entities from texts is a crucial task for pharmacology, and it is the basis for the ADR relation extraction task. The publicly available resources on this task include PubMed abstracts, social media, and other resources. Among these resources, social media data can reflect the reactions of drug users after taking medicine in real-time and update quickly. However, a very small quantity of annotated social media data leads to less research on these data. Moreover, social media data have colloquialism and informal vocabulary expression problems, which pose a major challenge for ADR named entity recognition (NER). In this work, we present an adversarial transfer learning architecture for the ADR NER task. Our model improves the performance on Twitter data (target resource) by incorporating biomedical domain information from PubMed (source resource). Additionally, we set the scale parameter in the final loss function to address the problem of bias in model training caused by imbalanced amounts of data. Without adding any additional manually designed features, our approach achieves state-of-the-art performance with an F1 on Twitter ADR data of 68.58%.