MedSTS：临床语义语篇相似度的资源
MedSTS: A Resource for Clinical Semantic Textual Similarity
摘要：
电子健康记录（EHR）的广泛采用使得利用EHR数据的广泛应用成为可能。然而，EHR数据的有意义的使用在很大程度上取决于我们有效地提取和整合嵌入在临床文本中的信息的能力，其中自然语言处理（NLP）技术是必不可少的。语义文本相似度（STS）是衡量文本片段之间语义相似性的指标，在许多自然语言处理应用中起着重要的作用。在一般的NLP领域，STS共享任务已经在各个领域提供了大量带有手动注释的文本片段对。在临床领域，STS可以使我们能够检测和消除冗余信息，这可能导致认知负担的减少和临床决策过程的改善。本文阐述了我们的努力，组装STS在医疗领域，MedSTS的资源。它包括从Mayo Clinic的临床语料库中收集的总共174，629个句子对。包含1，068个句子对的MedSTS子集（MedSTS_ann）由两名医学专家注释，语义相似性评分为0-5（低到高相似性）。我们进一步分析了MedSTS语料库中的医学概念，并在MedSTS_ann语料库上测试了四个STS系统。在未来，我们将通过发布MedSTS_ann语料库来组织一项共享任务，以激励社区解决现实世界的临床问题。
英文摘要：
The wide adoption of electronic health records (EHRs) has enabled a wide range of applications leveraging EHR data. However, the meaningful use of EHR data largely depends on our ability to efficiently extract and consolidate information embedded in clinical text where natural language processing (NLP) techniques are essential. Semantic textual similarity (STS) that measures the semantic similarity between text snippets plays a significant role in many NLP applications. In the general NLP domain, STS shared tasks have made available a huge collection of text snippet pairs with manual annotations in various domains. In the clinical domain, STS can enable us to detect and eliminate redundant information that may lead to a reduction in cognitive burden and an improvement in the clinical decision-making process. This paper elaborates our efforts to assemble a resource for STS in the medical domain, MedSTS. It consists of a total of 174,629 sentence pairs gathered from a clinical corpus at Mayo Clinic. A subset of MedSTS (MedSTS_ann) containing 1,068 sentence pairs was annotated by two medical experts with semantic similarity scores of 0-5 (low to high similarity). We further analyzed the medical concepts in the MedSTS corpus, and tested four STS systems on the MedSTS_ann corpus. In the future, we will organize a shared task by releasing the MedSTS_ann corpus to motivate the community to tackle the real world clinical problems.
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。
