基于局部增强的中文医疗命名实体识别模型

陈晶; 邢珂萱; 孟伟伦; 郭景峰; 冯建周

doi:10.11959/j.issn.1000-436x.2024117

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基于局部增强的中文医疗命名实体识别模型

学术论文 | 更新时间：2024-08-09

- 基于局部增强的中文医疗命名实体识别模型
- Chinese medical named entity recognition model based on local enhancement
- 通信学报 2024年45卷第7期页码：171-183
- 作者机构：
  
  1.广东海洋大学数学与计算机学院,广东湛江 524088
  2.河北省计算机虚拟技术与系统集成重点实验室,河北秦皇岛 066004
  3.燕山大学信息科学与工程学院,河北秦皇岛 066004
- 作者简介：
  
  [ "陈晶（1976- ），女，黑龙江哈尔滨人，博士，广东海洋大学教授、博士生导师，主要研究方向为社交网络分析和自然语言处理。" ]
  [ "邢珂萱（1998- ），女，黑龙江大庆人，燕山大学硕士生，主要研究方向为自然语言处理。" ]
  [ "孟伟伦（1998- ），男，河北衡水人，燕山大学博士生，主要研究方向为自然语言处理。" ]
  [ "郭景峰（1962- ），男，黑龙江哈尔滨人，博士，燕山大学教授、博士生导师，主要研究方向为数据库理论及应用。" ]
  [ "冯建周（1978- ），男，河北沧州人，博士，燕山大学副教授、硕士生导师，主要研究方向为自然语言处理与知识图谱。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62172352;42306218);中央省部共建基金资助项目(226Z0102G;226Z0305G);广东海洋大学科研启动基金资助项目;Guangdong Ocean University Scientific Research Start-up Fund Project(060302102304)
- DOI：10.11959/j.issn.1000-436x.2024117
  中图分类号： TP391.1
- 收稿日期：2024-03-11，
  
  修回日期：2024-06-06，
  
  纸质出版日期：2024-07-25
- 稿件说明：
移动端阅览
陈晶,邢珂萱,孟伟伦等.基于局部增强的中文医疗命名实体识别模型[J].通信学报,2024,45(07):171-183.

CHEN Jing,XING Kexuan,MENG Weilun,et al.Chinese medical named entity recognition model based on local enhancement[J].Journal on Communications,2024,45(07):171-183.
陈晶,邢珂萱,孟伟伦等.基于局部增强的中文医疗命名实体识别模型[J].通信学报,2024,45(07):171-183. DOI： 10.11959/j.issn.1000-436x.2024117.

CHEN Jing,XING Kexuan,MENG Weilun,et al.Chinese medical named entity recognition model based on local enhancement[J].Journal on Communications,2024,45(07):171-183. DOI： 10.11959/j.issn.1000-436x.2024117.

摘要

医学实体的识别往往受到其相邻上下文的影响，目前的命名实体识别方法通常依赖于BiLSTM捕捉文本中的全局依赖关系，缺乏对字符之间局部依赖关系的建模。针对这一问题，提出了一种基于局部增强的中文医疗命名实体识别模型LENER。首先，LENER使用包括字音、字形和语义在内的多源信息来丰富底层字符表征。然后，结合相对位置编码对滑动窗口划分出的序列片段进行局部注意力计算，并通过非线性计算融合局部信息和BiLSTM得到的全局信息。最后，对识别出的实体头部和尾部进行组合，进而提取出实体。实验结果表明，LENER模型具有良好的实体识别能力，与其他模型相比，LENER模型的

值提升了0.5%~2.0%。

Abstract

In the medical field

the recognition of medical entities is often influenced by their adjacent context

the current named entity recognition methods typically rely on BiLSTM to capture the global dependency relationships within text

lacking modeling of l

ocal dependencies between characters. To resolve this problem

a Chinese medical named entity recognition model LENER based on local enhancement was proposed. Firstly

the representation of characters was enriched by LENER utilizing multi-source information

including phonetic

graphic and semantic features. Secondly

relative position encoding was combined to perform local attention calculations on sequence segments divided by sliding windows

and local information was fused with global information obtained from BiLSTM through nonlinear computation. Finally

the recognized entity heads and tails were combined by LENER to extract the entities. The experimental results show that the LENER model has excellent entity recognition capabilities

and the

value is improved by 0.5% to 2% compared with other models.

关键词

Keywords

references

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