Negation Detection
12 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
Latest papers with no code
Revisiting subword tokenization: A case study on affixal negation in large language models
In this work, we measure the impact of affixal negation on modern English large language models (LLMs).
Effective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking
Our approach involves two key components in a pipeline-based model: (i) a data enrichment technique for enhancing both queries and documents during the first retrieval stage, and (ii) a novel re-ranking schema that uses a Transformer network in a setup adapted to this task by leveraging the structure of the CT documents.
A negation detection assessment of GPTs: analysis with the xNot360 dataset
Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension.
A Semantic Approach to Negation Detection and Word Disambiguation with Natural Language Processing
This study aims to demonstrate the methods for detecting negations in a sentence by uniquely evaluating the lexical structure of the text via word-sense disambiguation.
Improving negation detection with negation-focused pre-training
Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text.
Improving negation detection with negation-focused pre-training
Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text.
Flat and Nested Negation and Uncertainty Detection with PubMed BERT
We present a new negation detection dataset in two versions from clinical publications.
Scope resolution of predicted negation cues: A two-step neural network-based approach
We advocate for more research into the application of deep learning on negation detection and the effect of imperfect information on scope resolution.
IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes
To tackle this issue of availability of annotated data, a lot of research has been done on unsupervised domain adaptation that tries to generate systems for an unlabelled target domain data, given labeled source domain data.
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation
This paper describes our systems for negation detection and time expression recognition in SemEval 2021 Task 10, Source-Free Domain Adaptation for Semantic Processing.
