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基于语义分割的嵌套命名实体识别方法

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
命名实体识别旨在从非结构化文本中提取实体,实体之间通常存在嵌套结构.然而,以往的研究大多只关注平面命名实体的识别,而忽略了嵌套实体.因此本文提出一种基于语义分割的嵌套命名实体识别方法,该方法将嵌套命名实体识别任务表述为一个语义分割任务.首先,计算单词和单词之间的元素相似性、余弦相似性以及双线性相似性;然后将3种相似性特征拼接作为一个图像输入到语义分割模型中,得到单词和单词之间的关系矩阵;最后,从关系矩阵提取出嵌套实体.实验结果表明,本文方法可以有效地识别出嵌套实体,在公开嵌套命名实体识别数据集GENIA上的F1值达到80.0%,优于现有大多数嵌套实体识别方法.
Nested Named Entity Recognition Based on Semantic Segmentation
Named entity recognition aims to extract entities from an unstructured text,and a nested structure often exists be-tween entities.However,most of the previous studies only focused on the recognition of flat named entities while ignoring nested entities.Therefore,a nested named entity recognition method based on semantic segmentation is proposed,which describes the task of nested named entity recognition as a semantic segmentation task.First,we calculate the element similarity,cosine simi-larity and bilinear similarity between words and words.Then,the 3 similarity features are spliced as an image which will be input into the semantic segmentation model to obtain the relationship matrix between words and words.Finally,we extract nested entity from the relationship matrix.The experimental results show that the proposed method can effectively recognize nested entities,and the F1 value on the public nested named entity recognition dataset GENIA reaches 80.0%,which is superior to most existing nested entity recognition methods.

nested named entity recognitionrelation matrixsemantic segmentationcorrelation feature

崔少国、胡光平

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重庆师范大学计算机与信息科学学院,重庆 401331

嵌套命名实体识别 关系矩阵 语义分割 相关性特征

国家自然科学基金资助项目重庆市科技局自然基金资助项目重庆市科技局自然基金资助项目重庆市科技局自然基金资助项目教育部人文社科规划基金资助项目重庆市教委重点项目重庆市社会科学规划项目重庆师范大学人才基金项目

620030652022NSCQ-MSX29332022TFII-OFX0262cstc2019jscxmbdxX006122YJA870005KJZD-K2022005102022NDYB11920XLB004

2024

10.3969/j.issn.1006-2475.2024.02.011

计算机与现代化
江西省计算机学会 江西省计算技术研究所

计算机与现代化

CSTPCD
影响因子：0.472
ISSN：1006-2475
年,卷(期)：2024.(2)
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