Negation and Speculation Cue Detection
1 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Negation and Speculation Cue Detection
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The BioScope Corpus
Most implemented papers
NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution
adityak6798/Transformers-For-Negation-and-Speculation
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LREC 2020
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.
