English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Negation is an important characteristic of language, and a major component ofinformation extraction from text. This subtask is of considerable importance tothe biomedical domain. Over the years, multiple approaches have been exploredto 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 extensivelyreview previous literature addressing Negation Detection and Scope Resolutionacross the 3 datasets that have gained popularity over the years: the BioScopeCorpus, the Sherlock dataset, and the SFU Review Corpus. We then explore thedecision choices involved with using BERT, a popular transfer learning model,for this task, and report state-of-the-art results for scope resolution acrossall 3 datasets. Our model, referred to as NegBERT, achieves a token level F1score on scope resolution of 92.36 on the Sherlock dataset, 95.68 on theBioScope Abstracts subcorpus, 91.24 on the BioScope Full Papers subcorpus,90.95 on the SFU Review Corpus, outperforming the previous state-of-the-artsystems by a significant margin. We also analyze the model's generalizabilityto datasets on which it is not trained.

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