发表机构
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对现有医学关系抽取方法的缺陷，提出基于语言模型的PiPMRE流水线框架，经实验验证其在公开数据集上性能优于现有最优方法，少样本场景下也表现出色。
AI 中文摘要
医学关系抽取（Medical Relation Extraction，MRE）的核心是从医学文本中联合抽取实体及其关系，近年来受到广泛关注。现有研究多将MRE视为序列标注任务，但由于医学实体间关系复杂，该方法要么需要设计复杂的标注 schema，要么无法成功抽取多重关系。本研究从语言学视角重新审视该任务，提出一种基于语言模型的新型流水线框架PiPMRE以提升MRE性能。具体而言，PiPMRE包含关系生成器与关系过滤器两部分：给定文本后，生成器先输出多个关系三元组，再由过滤器对每个三元组打分，仅保留分数超过阈值的三元组作为最终结果。实现PiPMRE无需标注schema，而是用简单模板重构输入文本，确保实体与关系按上下文顺序生成。在两个公开数据集上的大量实验结果表明，PiPMRE性能更优：它较之前的最优方法平均提升了5.6个召回点和4.4个准确率点，在少样本设置中也展现出优越性。
英文摘要
Medical relation extraction (MRE) is commonly known for extracting entities and their relations jointly from a medical text, which has attracted considerable attention in recent years. Previous studies treat MRE as a sequence tagging task, which results in either a challenging design of the tagging schema or a failed extraction of multiple relations, due to intricate relationships among medical entities. In this work, we review the task from the linguistic perspective and propose a novel pipeline framework, PiPMRE, developed on language models to enhance MRE performance. Specifically, PiPMRE consists of a relation generator and a relation filter. Given a text, the generator first yields multiple relational triplets, and then the filter scores each triplet and retains only those that pass the borderline as the final results. Implementing PiPMRE requires no tagging schema; instead, we use a simple template to reformulate the input text, ensuring that entities and relations are generated in a contextual order. Extensive experimental results on two public datasets demonstrate the advancement of PiPMRE. It surpasses the previous state-of-the-art by an average of 5.6 recall points and 4.4 accuracy points. PiPMRE's superiority is also demonstrated in few-shot settings.
Journal refProceedings of the Annual Meeting of the Cognitive Science Society, Vol. 47, No. 0 (2025)
