Automatic Fact-Checking with Frame-Semantics
원문 초록
We propose a novel paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims, addressing the challenges posed by misinformation in today's information ecosystem. To support this approach, we introduce a pilot dataset of real-world claims extracted from PolitiFact, specifically annotated for large-scale structured data. This dataset underpins two case studies: the first investigates voting-related claims using the Vote semantic frame, while the second explores various semantic frames and data sources from the Organisation for Economic Co-operation and Development (OECD). Our findings demonstrate the effectiveness of frame semantics in improving evidence retrieval, indicating a meaningful advancement in automatic fact-checking capabilities. Finally, we conducted a survey of frames evoked in fact-checked claims, identifying high-impact frames to guide future research.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SUCEA: Reasoning-Intensive Retrieval for Adversarial Fact-checking through Claim Decomposition and Editing
Veracity: An Open-Source AI Fact-Checking System
RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation
The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers
In Crowd Veritas: Leveraging Human Intelligence To Fight Misinformation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.