基于跨度与类别增强的中文新闻命名实体识别

祁瑞艳; 李龙杰; 徐世琤; 马笠恭; 马志新

doi:10.11959/j.issn.2096-6652.202437

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基于跨度与类别增强的中文新闻命名实体识别

专题：社会计算与社会智能 | 更新时间：2025-01-24

- 基于跨度与类别增强的中文新闻命名实体识别
- Named entity recognition based on span and category enhancement for Chinese news
- 智能科学与技术学报 2024年6卷第4期页码：495-508
- 作者机构：
  
  1.兰州大学信息科学与工程学院，甘肃兰州 730000
  2.兰州市公安局，甘肃兰州 730000
- 作者简介：
  
  [ "祁瑞艳（2001- ），女，兰州大学信息科学与工程学院硕士生，主要研究方向为深度学习、自然语言处理等。" ]
  [ "李龙杰（1981- ），男，兰州大学信息科学与工程学院博士生，主要研究方向为大数据分析与应用、图机器学习、深度学习等。" ]
  [ "徐世琤（2000- ），男，兰州大学信息科学与工程学院硕士生，主要研究方向为深度学习、自然语言处理等。" ]
  [ "马笠恭（1969- ），男，本科，兰州市公安局网络安全保卫支队，主要研究方向为网络安全管理、网络安全防护等。" ]
  [ "马志新（1973- ），男，博士，兰州大学信息科学与工程学院教授，主要研究方向为大数据分析与应用、复杂网络分析、网络空间安全等。" ]
- 基金信息：
  
  甘肃省科技计划项目(23YFGA0005;21ZD8RA008)
- DOI：10.11959/j.issn.2096-6652.202437
  中图分类号： TP391
- 收稿日期：2024-08-13，
  
  修回日期：2024-09-24，
  
  纸质出版日期：2024-12-15
- 稿件说明：
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祁瑞艳,李龙杰,徐世琤等.基于跨度与类别增强的中文新闻命名实体识别[J].智能科学与技术学报,2024,06(04):495-508.

QI Ruiyan,LI Longjie,XU Shicheng,et al.Named entity recognition based on span and category enhancement for Chinese news[J].Chinese Journal of Intelligent Science and Technology,2024,06(04):495-508.
祁瑞艳,李龙杰,徐世琤等.基于跨度与类别增强的中文新闻命名实体识别[J].智能科学与技术学报,2024,06(04):495-508. DOI： 10.11959/j.issn.2096-6652.202437.

QI Ruiyan,LI Longjie,XU Shicheng,et al.Named entity recognition based on span and category enhancement for Chinese news[J].Chinese Journal of Intelligent Science and Technology,2024,06(04):495-508. DOI： 10.11959/j.issn.2096-6652.202437.

摘要

在新闻领域，识别命名实体涉及复杂的语法结构和长名称，这为确定实体边界带来了挑战，同时也引发了序列标注方法在预测长实体时的提前中断问题。为了应对这些挑战，提出了一种基于跨度与类别增强的中文新闻命名实体识别模型——SpaCE。该模型基于Transformer结构的双向编码器表示预训练模型（BERT），通过跨度预测和类别描述增强，提升了识别性能。在编码新闻文本信息的过程中，模型结合类别描述以增强语义知识，并采用基于跨度的解码方式来解决长实体预测中断问题。另外，通过精确标记的方法引入词边界信息，并优化实体匹配策略，有效减少了由跨度解码引起的非实体匹配情形。与基线模型相比，SpaCE在3个数据集上的性能均有所提升。另外，在无序文本上，SpaCE仍表现出了较强的命名实体识别能力，具有很好的鲁棒性。

Abstract

In the field of news

the identification of named entities is complicated by complex syntactic structures and long entity names

which pose challenges for determining entity boundaries and lead to interruptions in predicting long entities using sequence labeling methods. To address these challenges

a model named SpaCE (span and category enhancement for Chinese news named entity recognition) was proposed. This model was developed based on the bidirectional encoder representation pre-trained model with a Transformer structure (BERT) and was enhanced by span prediction and category description to improve recognition performance. During the encoding of news text information

category descriptions were incorporated to enhance semantic knowledge

and a span-based decoding method was adopted to address interruptions in predicting long entities. Furthermore

word boundary information was introduced through precise labeling

and the entity matching strategy was optimized

effectively reducing non-entity matching caused by span decoding. Compared to baseline models

SpaCE demonstrated improved performance on three datasets. Furthermore

SpaCE exhibits strong named entity recognition capabilities on disordered texts

indicating its robustness.

关键词

Keywords

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