핵심 개념
Fact-checking health claims using evidence-based approaches is crucial for verifying medical information online.
초록
Seeking health advice online has become common, but determining trustworthiness is challenging.
Fact-checking uses credible sources to assess the veracity of claims.
HealthFC dataset includes 750 health-related claims labeled by experts in German and English.
Dataset analysis highlights characteristics and challenges for NLP tasks.
Clinical trials are essential for testing hypotheses related to human health.
Automated fact-checking tools based on ML and NLP are still evolving.
Existing datasets lack clinical studies as primary knowledge sources for claim verification.
HealthFC dataset aims to address these gaps with bilingual data and rich annotations.
Directory:
Introduction
Online health seeking behavior and challenges in determining trustworthiness.
Data Extraction
"The dataset can be used for NLP tasks related to automated fact-checking."
"The daily requirement for the vitamin C is about 100 milligrams."
"Multiple clinical trials related to the same topic are commonly combined into a systematic review."
Related Work
Healthcare applications in AI and NLP, including biomedical NLP tasks like question answering.
Dataset Construction
Data source from Medizin Transparent, systematic approach to fact-checking, translation process.
Baselines
Pipeline vs. joint systems for evidence selection and veracity prediction using various base models.
통계
"The dataset can be used for NLP tasks related to automated fact-checking."
"The daily requirement for the vitamin C is about 100 milligrams."
"Multiple clinical trials related to the same topic are commonly combined into a systematic review."