Papers Negation Detection
“Negation Detection” 태그가 달린 논문 55편 · 필터 해제
ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding
We present ReXVQA, the largest and most comprehensive benchmark for visual question answering (VQA) in chest radiology, comprising approximately 696,000 questions paired with 160,000 chest X-rays studies across training,…
NegationNegation DetectionQuestion AnsweringVisual Question Answering+1Beyond Negation Detection: Comprehensive Assertion Detection Models for Clinical NLP
Assertion status detection is a critical yet often overlooked component of clinical NLP, essential for accurately attributing extracted medical facts. Past studies have narrowly focused on negation detection, leading to …
Domain AdaptationNegationNegation DetectionNER+1Revisiting 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). In affixal negation, the negated meaning is expressed through a negative morpheme, which is potentially challenging …
NegationNegation DetectionSensitivityEffective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking
Clinical trials (CTs) often fail due to inadequate patient recruitment. This paper tackles the challenges of CT retrieval by presenting an approach that addresses the patient-to-trials paradigm. Our approach involves two…
Descriptivenamed-entity-recognitionNamed Entity RecognitionNegation+3A 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. Our study assesses the negation detection performance of Generative Pre-trained Transformer (GPT) models, …
Natural Language UnderstandingNegationNegation DetectionSentenceA 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. The proposed framework examines all the unique feat…
NegationNegation DetectionSentenceSentiment Analysis+3Negation 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. One of the most i…
Information RetrievalNegationNegation DetectionRetrievalNegation Detection in Dutch Spoken Human-Computer Conversations
Proper recognition and interpretation of negation signals in text or communication is crucial for any form of full natural language understanding. It is also essential for computational approaches to natural language pro…
Natural Language UnderstandingNegationNegation DetectionTransfer LearningImproving 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. Recent work has shown that state-…
Data AugmentationDiversityNegationNegation DetectionRadiology 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 dia…
De-identificationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3Improving 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. Recent works show that state-of-t…
Data AugmentationDiversityNegationNegation DetectionFlat and Nested Negation and Uncertainty Detection with PubMed BERT
Negation and uncertainty detection is an oft-studied challenge in biomedical NLP. Annotation style for the task has not been standardized and as such, the existing datasets not only vary in domain but require various alg…
Multi-class ClassificationNegationNegation DetectionNADE: 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. However, despite the recent…
NegationNegation DetectionScope resolution of predicted negation cues: A two-step neural network-based approach
Neural network-based methods are the state of the art in negation scope resolution. However, they often use the unrealistic assumption that cue information is completely accurate. Even if this assumption holds, there rem…
NegationNegation DetectionNegation Scope ResolutionSemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing
This paper presents the Source-Free Domain Adaptation shared task held within SemEval-2021. The aim of the task was to explore adaptation of machine-learning models in the face of data sharing constraints. Specifically, …
Domain AdaptationNegationNegation DetectionSource-Free Domain Adaptation+2IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes
Recent progress in deep learning has primarily been fueled by the availability of large amounts of annotated data that is obtained from highly expensive manual annotating pro-cesses. To tackle this issue of availability …
Data AugmentationDomain AdaptationNegationNegation Detection+2The 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. We show that self-training, active learning and data…
Active LearningData AugmentationDomain AdaptationNegation+2MedAI 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 sourc…
Domain AdaptationNegationNegation DetectionSource-Free Domain Adaptation+1Negation 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. Despite many advances in text analytics, negation resolution remains an acute and continuously researched question in…
Machine TranslationNegationNegation DetectionNegation Scope Resolution+2EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain
Transformer-based neural language models have led to breakthroughs for a variety of natural language processing (NLP) tasks. However, most models are pretrained on general domain data. We propose a methodology to produce…
NegationNegation DetectionRelationRelation Extraction+1