Negation Detection
12 papers with code • 0 benchmarks • 4 datasets
Negation detection is the task of identifying negation cues in text.
Benchmarks
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Libraries
Use these libraries to find Negation Detection models and implementationsDatasets
ShARe/CLEF 2014: Task 2 Disorders
This is not a Dataset
Latest papers with no code
MedAI at SemEval-2021 Task 10: Negation-aware Pre-training for Source-free Negation Detection Domain Adaptation
Due to the increasing concerns for data privacy, source-free unsupervised domain adaptation attracts more and more research attention, where only a trained source model is assumed to be available, while the labeled source data remain private.
Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish.
Negation is a linguistic universal that poses difficulties for cognitive and computational processing.
Automatic Extraction of Ranked SNP-Phenotype Associations from Literature through Detecting Neural Candidates, Negation and Modality Markers
Recently, very few methods have been developed for extracting mutation-diseases associations.
Negation Detection for Clinical Text Mining in Russian
This paper is devoted to a module of negation detection.
Speculation and Negation detection in French biomedical corpora
We reach up to 97. 21 {\%} and 91. 30 {\%} F-measure for the detection of negation and speculation cues, respectively, using CRFs.
A Study of the Effect of Resolving Negation and Sentiment Analysis in Recognizing Text Entailment for Arabic
This paper aims to increase the entailment accuracy for Arabic texts by resolving negation of the text-hypothesis pair and determining the polarity of the text-hypothesis pair whether it is Positive, Negative or Neutral.
Joint Entity Extraction and Assertion Detection for Clinical Text
Most of the existing systems treat this task as a pipeline of two separate tasks, i. e., named entity recognition (NER) and rule-based negation detection.
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data.
Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection
We use a broad coverage, linguistically precise English Resource Grammar (ERG) to detect negation scope in sentences taken from pathology reports.
