NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution

Negation is an important characteristic of language, and a major component of information extraction from text. This subtask is of considerable importance to the biomedical domain. Over the years, multiple approaches have been explored to address this problem: simple rule-based systems, Machine Learning classifiers, Conditional Random Field Models, CNNs and more recently BiLSTMs. In this paper, we look at applying Transfer Learning to this problem. First, we extensively review previous literature addressing Negation Detection and Scope Resolution across the 3 datasets that have gained popularity over the years: BioScope Corpus, the Sherlock dataset, and the SFU Review Corpus. We then explore the decision choices involved with using BERT, a popular transfer learning model, for this task, and report a new state-of-the-art for scope resolution across all 3 datasets. Our model, referred to as NegBERT, achieves a token level F1 score on scope resolution of 92.36 on the Sher-lock dataset, 95.68 on the BioScope Abstracts, 91.24 on the BioScope Full Papers, 90.95 on the SFU dataset, out-performing the previous state-of-the-art by a significant margin. We also analyze the model's generalizability to datasets on which it is not trained.