Unsupervised Domain Adaptation for Clinical Negation Detection
Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationNegationNegation DetectionUnsupervised Domain AdaptationSimilar Papers 제목 키워드 기반
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 sourc…
Domain AdaptationNegationNegation DetectionSource-Free Domain Adaptation+1IITK 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+2Negation Detection in Clinical Reports Written in German
An important subtask in clinical text mining tries to identify whether a clinical finding is expressed as present, absent or unsure in a text. This work presents a system for detecting mentions of clinical findings that …
Named Entity Recognition (NER)NegationNegation DetectionRelation ExtractionBeyond 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+1NAST: Improving Negation Handling in Medical Vision-Language Models through Negation-Aware Selective Training
Negation is a fundamental linguistic operation in clinical reporting, yet vision-language models (VLMs) frequently fail to distinguish affirmative from negated medical statements. To systematically characterize this limi…