广西师范大学学报（自然科学版） ›› 2025, Vol. 43 ›› Issue (3): 1-11.doi: 10.16088/j.issn.1001-6600.2024092703

• CCIR2024 •    下一篇

融合边界交互信息的命名实体识别方法

何安康1,2,3, 陈艳平1,2,3*, 扈应1,2,3, 黄瑞章1,2,3, 秦永彬1,2,3   

  1. 1.贵州大学文本计算与认知智能教育部工程研究中心, 贵州贵阳 550025;
    2.公共大数据国家重点实验室(贵州大学), 贵州贵阳 550025;
    3.贵州大学计算机科学与技术学院, 贵州贵阳 550025
  • 收稿日期:2024-09-27 修回日期:2024-12-20 出版日期:2025-05-05 发布日期:2025-05-14
  • 通讯作者: 陈艳平(1980—), 男, 贵州长顺人, 贵州大学教授, 博士。E-mail: ypench@gmail.com
  • 基金资助:
    贵州省科学技术基金重点项目(〔2024〕003); 国家重点研发计划(2023YFC3304500); 国家自然科学基金(62166007)

Fusing Boundary Interaction Information for Named Entity Recognition

HE Ankang1,2,3, CHEN Yanping1,2,3*, HU Ying1,2,3, HUANG Ruizhang1,2,3, QIN Yongbin1,2,3   

  1. 1. Text Computing and Cognitive Intelligence Engineering Research Center of the Ministry of Education, Guizhou University, Guiyang Guizhou 550025, China;
    2. State Key Laboratory of Public Big Data (Guizhou University), Guiyang Guizhou 550025, China;
    3. College of Computer Science and Technology, Guizhou University, Guiyang Guizhou 550025, China
  • Received:2024-09-27 Revised:2024-12-20 Online:2025-05-05 Published:2025-05-14

PDF (PC)

138

摘要： 命名实体识别是自然语言处理领域中的一项基本任务,旨在识别和分类文本中的命名实体。目前,基于跨度的方法在实体识别方面取得一定进展,但这些方法往往忽视了候选跨度的质量差异。针对该问题,本文提出一种融合边界交互信息的命名实体识别方法。该方法通过一个边界交互模块评估边界间的语义关联和交互强度,生成边界交互信息矩阵,用于识别边界间潜在的语义联系,引导模型识别和标记出高质量的候选跨度。此外,该方法集成多尺度空洞卷积模块,利用跨度之间的语义关系来减轻非实体噪声的影响。实验表明,本文方法在ACE2005中文数据集、ACE2005英文数据集和Weibo数据集上的F1值分别达到89.78%、87.37%和72.10%,与基准模型相比分别提升0.67、0.95和0.69个百分点,验证了该方法对命名实体识别的有效性。

关键词: 自然语言处理, 命名实体识别, 信息抽取, 边界交互

Abstract: As a basic task in natural language processing, named entity recognition (NER) can effectively identify and classify named entities in text. Some progress has been made in entity recognition with span-based methods, but the quality differences between candidate spans are often overlooked. To tackle the problem, a named entity recognition method that fuses boundary interaction information is proposed. A boundary interaction module is used to evaluate the semantic associations and interaction strengths between boundaries, and a boundary interaction information matrix is generated. This matrix is used to identify potential semantic connections between boundaries, guiding the model to recognize and mark high-quality candidate spans. Additionally, a multi-scale dilated convolution module is integrated to reduce the impact of non-entity noise by utilizing the semantic relationships between spans. It is demonstrated through experiments that the method achieves F1 scores of 89.78%, 87.37%, and 72.10% on the ACE2005 Chinese dataset, ACE2005 English dataset, and Weibo dataset, respectively. These results represent improvements of 0.67, 0.95, and 0.69 percentage points over baseline models, validating the effectiveness of the proposed method for named entity recognition.

Key words: natural language processing, named entity recognition, information extraction, boundary interaction

中图分类号:  TP391.1

[1] 赵山, 罗睿, 蔡志平. 中文命名实体识别综述[J]. 计算机科学与探索, 2022, 16(2): 296-304. DOI: 10.3778/j.issn.1673-9418.2107031.
[2]乔勇鹏, 于亚新, 刘树越, 等. 图卷积增强多路解码的实体关系联合抽取模型[J]. 计算机研究与发展, 2023, 60(1): 153-166. DOI: 10.7544/issn1000-1239.202110767.
[3]冀相冰, 朱艳辉, 詹飞, 等. 基于门控多层次注意机制的事件主体抽取[J]. 计算机应用与软件, 2021, 38(9): 173-179, 187. DOI: 10.3969/j.issn.1000-386x.2021.09.027.
[4]秦贺然, 刘浏, 李斌, 等. 融入实体特征的典籍自动分类研究[J]. 数据分析与知识发现, 2019, 3(9): 68-76. DOI: 10.11925/infotech.2096-3467.2019.0135.
[5]肖新凤, 李石君, 余伟, 等. 基于改进seq2seq模型的英汉翻译研究[J]. 计算机工程与科学, 2019, 41(7): 1257-1265. DOI: 10.3969/j.issn.1007-130X.2019.07.016.
[6]俞鸿魁, 张华平, 刘群, 等. 基于层叠隐马尔可夫模型的中文命名实体识别[J]. 通信学报, 2006, 27(2): 87-94. DOI: 10.3321/j.issn:1000-436X.2006.02.013.
[7]胡文博, 都云程, 吕学强, 等. 基于多层条件随机场的中文命名实体识别[J]. 计算机工程与应用, 2009, 45(1): 163-165, 227. DOI: 10.3778/j.issn.1002-8331.2009.01.051.
[8]陈启丽, 黄冠和, 王元卓, 等. 一种融合注意力机制的自适应实体识别方法[J]. 中文信息学报, 2021, 35(6): 55-62, 73. DOI: 10.3969/j.issn.1003-0077.2021.06.006.
[9]SHIBUYA T, HOVY E. Nested named entity recognition via second-best sequence learning and decoding[J]. Transactions of the Association for Computational Linguistics, 2020, 8: 605-620. DOI: 10.1162/tacl_a_00334.
[10]WANG J, SHOU L D, CHEN K, et al. Pyramid: a layered model for nested named entity recognition[C]// Proceedings of the 58th annual meeting of the association for computational linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2020: 5918-5928. DOI: 10.18653/v1/2020.acl-main.525.
[11]LU W, ROTH D. Joint mention extraction and classification with mention hypergraphs[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2015: 857-867. DOI: 10.18653/v1/D15-1102.
[12]MUIS A O, LU W. Learning to recognize discontiguous entities[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2016: 75-84. DOI: 10.18653/v1/D16-1008.
[13]WANG B L, LU W. Neural segmental hypergraphs for overlapping mention recognition[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2018: 204-214. DOI: 10.18653/v1/D18-1019.
[14]LEWIS M, LIU Y H, GOYAL N, et al. BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2020: 7871-7880. DOI: 10.18653/v1/2020.acl-main.703.
[15]CUI L Y, WU Y, LIU J, et al. Template-based named entity recognition using BART[C]// Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Stroudsburg, PA: Association for Computational Linguistics, 2021: 1835-1845. DOI: 10.18653/v1/2021.findings-acl.161.
[16]FEI H, JI D H, LI B B, et al. Rethinking boundaries: end-to-end recognition of discontinuous mentions with pointer networks[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(14): 12785-12793. DOI: 10.1609/aaai.v35i14.17513.
[17]扈应, 陈艳平, 黄瑞章, 等. 结合CRF的边界组合生物医学命名实体识别[J]. 计算机应用研究, 2021, 38(7): 2025-2031. DOI: 10.19734/j.issn.1001-3695.2020.09.0238.
[18]黄蓉, 陈艳平, 扈应, 等. 结合实体边界线索的中文命名实体识别方法[J]. 计算机工程与应用, 2024, 60(6): 199-206. DOI: 10.3778/j.issn.1002-8331.2211-0119.
[19]YU J T, BOHNET B, POESIO M. Named entity recognition as dependency parsing[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2020: 6470-6476. DOI: 10.18653/v1/2020.acl-main.577.
[20]DOZAT T, MANNING C D. Deep biaffine attention for neural dependency parsing[EB/OL]. (2017-03-10)[2024-09-27]. https://arxiv.org/abs/1611.01734. DOI: 10.48550/arXiv.1611.01734.
[21]SHEN Y L, MA X Y, TAN Z Q, et al. Locate and label: a two-stage identifier for nested named entity recognition[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2021: 2782-2794. DOI: 10.18653/v1/2021.acl-long.216.
[22]LI J Y, FEI H, LIU J, et al. Unified named entity recognition as word-word relation classification[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(10): 10965-10973. DOI: 10.1609/aaai.v36i10.21344.
[23]JOHNSON J M, KHOSHGOFTAAR T M. Survey on deep learning with class imbalance[J]. Journal of Big Data, 2019, 6(1): 27. DOI: 10.1186/s40537-019-0192-5.
[24]WAN J C, RU D Y, ZHANG W N, et al. Nested named entity recognition with span-level graphs[C]// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2022: 892-903. DOI: 10.18653/v1/2022.acl-long.63.
[25]COLLOBERT R, WESTON J, BOTTOU L, et al. Natural language processing (almost) from scratch[J]. The Journal of Machine Learning Research, 2011, 12: 2493-2537.
[26]YAN H, DENG B C, LI X N, et al. TENER: adapting transformer encoder for named entity recognition[EB/OL]. (2019-12-10)[2024-09-27]. https://arxiv.org/abs/1911.04474. DOI: 10.48550/arXiv.1911.04474.
[27]LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]// Proceedings of the Eighteenth International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann Publishers Inc., 2001: 282-289.
[28]WANG Y C, YU B W, ZHU H S, et al. Discontinuous named entity recognition as maximal clique discovery[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2021: 764-774. DOI: 10.18653/v1/2021.acl-long.63.
[29]VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Advances in Neural Information Processing Systems 30 (NIPS 2017). Red Hook, NY: Curran Associates Inc., 2017: 6000-6010.
[30]WADDEN D, WENNBERG U, LUAN Y, et al. Entity, relation, and event extraction with contextualized span representations[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA: Association for Computational Linguistics, 2019: 5784-5789. DOI: 10.18653/v1/D19-1585.
[31]LI F, LIN Z C, ZHANG M S, et al. A span-based model for joint overlapped and discontinuous named entity recognition[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2021: 4814-4828. DOI: 10.18653/v1/2021.acl-long.372.
[32]NIAN Y M, CHENY P, QIN Y B, et al. A joint model for entity boundary detection and entity span recognition[J]. Journal of King Saud University-Computer and Information Sciences, 2022, 34(10, Part A): 8362-8369. DOI: 10.1016/j.jksuci.2022.08.016.
[33]SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
[34]HENDRYCKS D, GIMPEL K. Gaussian error linear units (GELUs)[EB/OL]. (2023-06-06)[2024-09-27]. https://arxiv.org/abs/1606.08415. DOI: 10.48550/arXiv.1606.08415.
[35]CHEN Y P, WU Y F, QIN Y B, et al. Recognizing nested named entity based on the neural network boundary assembling model[J]. IEEE Intelligent Systems, 2020, 35(1): 74-81. DOI: 10.1109/MIS.2019.2952334.
[36]SHEN Y L, SONG K T, TAN X, et al. DiffusionNER: boundary diffusion for named entity recognition[C]// Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2023: 3875-3890. DOI: 10.18653/v1/2023.acl-long.215.
[37]ZHU E W, LI J P. Boundary smoothing for named entity recognition[C]// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2022: 7096-7108. DOI: 10.18653/v1/2022.acl-long.490.
[38]WANG S H, SUN X F, LI X Y, et al. GPT-NER: named entity recognition via large language models[EB/OL]. (2023-10-07)[2024-09-27]. https://arxiv.org/abs/2304.10428. DOI: 10.48550/arXiv.2304.10428.
[1] 卢展跃, 陈艳平, 杨卫哲, 黄瑞章, 秦永彬. 基于掩码注意力与多特征卷积网络的关系抽取方法[J]. 广西师范大学学报（自然科学版）, 2025, 43(3): 12-22.
[2] 郝雅茹, 董力, 许可, 李先贤. 预训练语言模型的可解释性研究进展[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 59-71.
[3] 周圣凯, 富丽贞, 宋文爱. 基于深度学习的短文本语义相似度计算模型[J]. 广西师范大学学报（自然科学版）, 2022, 40(3): 49-56.
[4] 万福成,马宁,何向真. 融合事件特征及语义角色标注的藏文信息抽取技术[J]. 广西师范大学学报（自然科学版）, 2018, 36(2): 18-23.
[5] 宋俊, 韩啸宇, 黄宇, 黄廷磊, 付琨. 一种面向实体的演化式多文档摘要生成方法[J]. 广西师范大学学报（自然科学版）, 2015, 33(2): 36-41.
[6] 张春元. 基于CRFs的新闻网页主题内容自动抽取方法[J]. 广西师范大学学报（自然科学版）, 2011, 29(1): 138-142.
[7] 程显毅, 潘燕, 朱倩, 孙萍. 面向事件的多文档文摘生成算法的研究[J]. 广西师范大学学报（自然科学版）, 2011, 29(1): 147-150.
[8] 黄锋, 吴华瑞. 一种自适应的Web信息抽取规则自动生成方法[J]. 广西师范大学学报（自然科学版）, 2010, 28(1): 127-130.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 广西师范大学学报（自然科学版）编辑部
地址：广西桂林市三里店育才路15号　邮编：541004
电话：0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发