基于深度学习的医学实体和关系联合抽取研究综述

Review of Joint Extraction of Medical Entities and Relationships Based on Deep Learning

命名实体识别与关系抽取作为医学领域信息抽取的核心任务,能够从非结构化或半结构化的文本中自动识别实体边界、实体类型以及实体之间的关系。不仅能够促进知识的发现与整合,应用于临床决策,加强药物的发现和再利用,还可以助力公共卫生监测和疾病预防。回顾了实体识别和关系抽取的发展历程,介绍了常用评价指标和医学领域实体关系联合抽取数据集,指出目前联合抽取领域存在医学文本结构比较复杂、实体关系重叠句子抽取率低等问题。根据这些问题,进一步探讨了基于深度学习的实体关系联合抽取方法在医学领域上的应用。这些方法根据模型解码的方式主要分为基于共享参数的联合抽取模型和基于联合解码的联合抽取模型,从问题解决角度对不同的模型的优缺点进行探讨分析和总结。讨论了医学领域实体关系抽取面临的挑战和未来的研究方向。

Named entity recognition and relationship extraction are core tasks in the field of medical information extrac-tion.They enable the automatic identification of entities,entity types,and relationships between entities from unstructured or semi-structured text.This capability not only facilitates the discovery and integration of knowledge,application in clini-cal decision-making,and enhancement of drug discovery and repurposing,but also supports public health monitoring and disease prevention.This article begins by reviewing the development of entity recognition and relationship extraction,introducing common evaluation metrics and datasets for joint entity and relationship extraction in the medical field.It highlights current challenges in the field,such as the complexity of medical text structures and the low accuracy of joint extraction.Building on these issues,the article further explores the application of deep learning-based methods for joint entity and relationship extraction in the medical field.These methods are primarily categorized into joint extraction models based on shared parameters and those based on joint decoding.The article discusses and summarizes the advantages and disadvantages of different models from a problem-solving perspective.Finally,the article discusses the challenges in joint entity and relationship extraction within the medical field and suggests future research directions.