PiPMRE: A Pipeline Based on Language Model for Medical Relation Extraction

PiPMRE：一种基于语言模型的医学关系抽取流水线框架

Abstract: 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. 摘要： 医学关系抽取（MRE）通常指从医学文本中联合抽取实体及其关系，近年来受到了广泛关注。

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. 以往的研究将 MRE 视为序列标注任务，但由于医学实体之间存在复杂的关系，这往往导致标注模式设计困难，或者无法成功抽取多重关系。

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. 在这项工作中，我们从语言学角度重新审视了该任务，并提出了一种基于语言模型的新型流水线框架 PiPMRE，旨在提升 MRE 的性能。

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. 具体而言，PiPMRE 由关系生成器和关系过滤器组成。给定一段文本，生成器首先产生多个关系三元组，随后过滤器对每个三元组进行评分，仅保留达到阈值的三元组作为最终结果。

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. 实现 PiPMRE 不需要任何标注模式；相反，我们使用简单的模板来重构输入文本，确保实体和关系能够按上下文顺序生成。

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. 在两个公开数据集上的大量实验结果证明了 PiPMRE 的先进性。它在召回率和准确率上分别比此前的最优模型平均提升了 5.6 个百分点和 4.4 个百分点。PiPMRE 在少样本（few-shot）场景下的优越性也得到了验证。