Joint extraction of Chinese medical entities and relations based on RoBERTa and single-module global pointer.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Dongmei Li, Yu Yang, Jinman Cui, Xianghao Meng, Jintao Qu, Zhuobin Jiang, Yufeng Zhao
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引用次数: 0

Abstract

Background: Most Chinese joint entity and relation extraction tasks in medicine involve numerous nested entities, overlapping relations, and other challenging extraction issues. In response to these problems, some traditional methods decompose the joint extraction task into multiple steps or multiple modules, resulting in local dependency in the meantime.

Methods: To alleviate this issue, we propose a joint extraction model of Chinese medical entities and relations based on RoBERTa and single-module global pointer, namely RSGP, which formulates joint extraction as a global pointer linking problem. Considering the uniqueness of Chinese language structure, we introduce the RoBERTa-wwm pre-trained language model at the encoding layer to obtain a better embedding representation. Then, we represent the input sentence as a third-order tensor and score each position in the tensor to prepare for the subsequent process of decoding the triples. In the end, we design a novel single-module global pointer decoding approach to alleviate the generation of redundant information. Specifically, we analyze the decoding process of single character entities individually, improving the time and space performance of RSGP to some extent.

Results: In order to verify the effectiveness of our model in extracting Chinese medical entities and relations, we carry out the experiments on the public dataset, CMeIE. Experimental results show that RSGP performs significantly better on the joint extraction of Chinese medical entities and relations, and achieves state-of-the-art results compared with baseline models.

Conclusion: The proposed RSGP can effectively extract entities and relations from Chinese medical texts and help to realize the structure of Chinese medical texts, so as to provide high-quality data support for the construction of Chinese medical knowledge graphs.

基于 RoBERTa 和单模块全局指针的中医实体和关系联合提取。
背景:大多数中文医学实体和关系联合抽取任务涉及大量嵌套实体、重叠关系和其他具有挑战性的抽取问题。针对这些问题,一些传统方法将联合抽取任务分解为多个步骤或多个模块,导致在此过程中出现局部依赖:为了解决这一问题,我们提出了一种基于 RoBERTa 和单模块全局指针的中医实体和关系联合提取模型,即 RSGP,它将联合提取表述为一个全局指针链接问题。考虑到中文语言结构的独特性,我们在编码层引入了 RoBERTa-wwm 预训练语言模型,以获得更好的嵌入表示。然后,我们将输入句子表示为三阶张量,并对张量中的每个位置进行评分,为后续的三元组解码过程做好准备。最后,我们设计了一种新颖的单模块全局指针解码方法,以减少冗余信息的产生。具体来说,我们单独分析了单字符实体的解码过程,在一定程度上提高了 RSGP 的时间和空间性能:为了验证我们的模型在提取中医实体和关系方面的有效性,我们在公共数据集 CMeIE 上进行了实验。实验结果表明,与基线模型相比,RSGP 在联合提取中医实体和关系方面的表现明显更好,达到了最先进的效果:结论:所提出的 RSGP 能有效地从中医文本中提取实体和关系,帮助实现中医文本的结构化,从而为中医知识图谱的构建提供高质量的数据支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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