用语言模型构建新框架，提升医疗关系抽取准确率与召回率。
PiPMRE: A Pipeline Based on Language Model for Medical Relation Extraction
- 分两步生成并筛选关系三元组，无需复杂标注体系。
- 在两个数据集上平均提升5.6%召回率和4.4%准确率。
- 适合需要高精度医疗信息提取的研究者与应用开发。
医疗关系抽取（MRE）旨在从医学文本中联合提取实体及其关系，近年来备受关注。以往研究多将其视为序列标注任务，导致标注体系设计复杂或难以捕捉多重关系。本文从语言学视角出发，提出基于语言模型的新型流水线框架PiPMRE，包含关系生成器与关系过滤器。给定文本后，生成器首先产出多个关系三元组，过滤器则对每个三元组评分并保留通过阈值的作为最终结果。该方法无需标注模板，仅通过简单提示重构输入，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实验表明，PiPMRE平均优于此前最先进方法5.6个百分点召回率和4.4个百分点准确率，且在少样本设置下仍具优势。
原文摘要 · Abstract (English)
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.
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