Negation detection is the task of identifying negation cues in text.
Detecting negation and speculation in language has been a task of considerable interest to the biomedical community, as it is a key component of Information Extraction systems from Biomedical documents.
In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow.
Speculation is a naturally occurring phenomena in textual data, forming an integral component of many systems, especially in the biomedical information retrieval domain.
Ranked #1 on Negation Scope Resolution on BioScope : Abstracts (using extra training data)
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.
Ranked #1 on Negation Scope Resolution on *sem 2012 Shared Task: Sherlock Dataset (using extra training data)
The TREC-PM challenge aims for advances in the field of information retrieval applied to precision medicine.
Ranked #1 on Information Retrieval on TREC-PM