  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: 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: the 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 state-of-the-art results 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 Sherlock dataset, 95.68 on the
BioScope Abstracts subcorpus, 91.24 on the BioScope Full Papers subcorpus,
90.95 on the SFU Review Corpus, outperforming the previous state-of-the-art
systems by a significant margin. We also analyze the model's generalizability
to datasets on which it is not trained.
