Negation Detection
11 papers with code • 0 benchmarks • 4 datasets
Negation detection is the task of identifying negation cues in text.
Benchmarks
These leaderboards are used to track progress in Negation Detection
Libraries
Use these libraries to find Negation Detection models and implementationsDatasets
ShARe/CLEF 2014: Task 2 Disorders
This is not a Dataset
Most implemented papers
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.
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.
Resolving the Scope of Speculation and Negation using Transformer-Based Architectures
adityak6798/Transformers-For-Negation-and-Speculation
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Speculation is a naturally occurring phenomena in textual data, forming an integral component of many systems, especially in the biomedical information retrieval domain.
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.
Multitask Learning of Negation and Speculation using Transformers
adityak6798/Transformers-For-Negation-and-Speculation
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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.
SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing
Participants are then tested on data representing a new (target) domain.
NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations
Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations.
Radiology Text Analysis System (RadText): Architecture and Evaluation
Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis.
Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods
As structured data are often insufficient, labels need to be extracted from free text in electronic health records when developing models for clinical information retrieval and decision support systems.
