一种基于实体的声明抽取流程，用于真实世界生物医学事实核查
An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking
AI总结：
本文提出面向医学推文的声明抽取流程，结合命名实体识别与实体链接，研究自动实体检测和术语规范化对真实世界生物医学事实核查的影响。
摘要：
现有的生物医学声明事实核查模型通常在合成数据或措辞规范的数据上训练，很难迁移到社交媒体内容上。这种不匹配可以通过将社交媒体输入调整为模仿常见训练声明所具有的聚焦性来缓解。为此，Wuehrl & Klinger (2022) 提出基于文本中的医学实体来抽取简洁声明。然而，他们的研究有两个局限：第一，它依赖人工标注的实体。因此，无法评估其在真实世界应用中的可行性，因为这需要自动检测相关实体。第二，他们用原始词元来表示声明实体。这构成了术语不匹配，可能限制事实核查性能。为了理解这两个挑战，我们提出了一种面向医学推文的声明抽取流程，该流程结合了命名实体识别和通过实体链接进行的术语规范化。我们表明，与使用人工标注相比，自动命名实体识别确实会导致性能下降，但事实核查性能相比直接输入未修改推文仍有显著提升。然而，将实体规范化为其标准形式并没有提升性能。
英文摘要：
Existing fact-checking models for biomedical claims are typically trained on synthetic or well-worded data and hardly transfer to social media content. This mismatch can be mitigated by adapting the social media input to mimic the focused nature of common training claims. To do so, Wuehrl & Klinger (2022) propose to extract concise claims based on medical entities in the text. However, their study has two limitations: First, it relies on gold-annotated entities. Therefore, its feasibility for a real-world application cannot be assessed since this requires detecting relevant entities automatically. Second, they represent claim entities with the original tokens. This constitutes a terminology mismatch which potentially limits the fact-checking performance. To understand both challenges, we propose a claim extraction pipeline for medical tweets that incorporates named entity recognition and terminology normalization via entity linking. We show that automatic NER does lead to a performance drop in comparison to using gold annotations but the fact-checking performance still improves considerably over inputting the unchanged tweets. Normalizing entities to their canonical forms does, however, not improve the performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
