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Negation detection is the task of identifying negation cues in text.
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An open-source tool for negation detection: a maximum-margin approach
This paper presents an open-source toolkit for negation detection.
Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification
In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow.
HIERARCHICAL STRUCTURE NEGATION DETECTION TEXT SUMMARIZATION
Multitask Learning of Negation and Speculation using Transformers
• adityak6798/Transformers-For-Negation-and-Speculation
• 
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.
Resolving the Scope of Speculation and Negation using Transformer-Based Architectures
• adityak6798/Transformers-For-Negation-and-Speculation
•
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)
INFORMATION RETRIEVAL NEGATION SCOPE RESOLUTION SPECULATION SCOPE RESOLUTION
NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution
LREC 2020
• adityak6798/Transformers-For-Negation-and-Speculation
•
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)
HPI-DHC at TREC 2018 Precision Medicine Track
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
DOCUMENT CLASSIFICATION INFORMATION RETRIEVAL NEGATION DETECTION TOPIC MODELS