SpanMRC: Query with Entity Length for MRC-Based Named Entity Recognition

被引:0
|
作者
Wu, Hao [1 ]
Li, Xianxian [1 ,2 ]
Liu, Peng [1 ,2 ]
Wang, Li-e [1 ,2 ]
Yang, Danping [1 ]
Zhou, Aoxiang [1 ]
机构
[1] Guangxi Normal Univ, Sch Comp Sci & Engn, Guilin 541004, Peoples R China
[2] Guangxi Normal Univ, Key Lab Educ Blockchain & Intelligent Technol, Minist Educ, Guilin 541004, Peoples R China
来源
ADVANCED INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PT IV, ICIC 2024 | 2024年 / 14878卷
基金
中国国家自然科学基金;
关键词
Entity recognition; Machine reading comprehension;
D O I
10.1007/978-981-97-5672-8_24
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The task of named entity recognition (NER) is to identify predefined types of entities from the given text. When entities overlap, this problem is referred to as nested named entity recognition (Nested NER). Recent researches have treated the NER task as a machine reading comprehension (MRC) task and proposed a lot of effective methods based on to extract flat and nested entities simultaneously. However, traditional MRC-based methods to rely on entity types in the design of questions are not justified due to neglecting the possibility of nested relationships between entities of the same type, and their efficiency is directly affected by the size of entity types. Motivated by the above problems, we apply the MRC framework to the NER task from the perspective of entity lengths but not entity types and propose a novel MRCbased method for NER called SpanMRC. Our approach can tackle flat and nested entities, and it is equally effective for nested entities of the same type. Moreover, the construction of questions is independent of the entity types, which can effectively improve the efficiency of the algorithm as the number of entity types increases. Extensive experiments demonstrate the superiority of SpanMRC in terms of entity recognition accuracy and algorithmic efficiency.
引用
收藏
页码:281 / 293
页数:13
相关论文
共 50 条
  • [31] Chinese named entity recognition based on adaptive transformer
    Yan Yang
    Yin, Guozhe
    2022 5TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING, MLNLP 2022, 2022, : 327 - 331
  • [32] Knowledge-based Named Entity Recognition in Polish
    Pohl, Aleksander
    2013 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), 2013, : 145 - 151
  • [33] Chinese Named Entity Recognition and Disambiguation Based on Wikipedia
    Yu Miao
    Lv Yajuan
    Liu Qun
    Su Jinsong
    Xiong Hao
    NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, 2012, 333 : 272 - 283
  • [34] A named entity recognition model based on ensemble learning
    Zhu, Xinghui
    Zou, Zhuoyang
    Qiao, Bo
    Fang, Kui
    Chen, Yiming
    JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2021, 21 (02) : 475 - 486
  • [35] Pattern based bootstrapping method for named entity recognition
    Ekbal, Asif
    Bandyopadhyay, Sivaji
    PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON ADVANCES IN PATTERN RECOGNITION, 2007, : 349 - +
  • [36] GNN-Based Multimodal Named Entity Recognition
    Gong, Yunchao
    Lv, Xueqiang
    Yuan, Zhu
    You, Xindong
    Hu, Feng
    Chen, Yuzhong
    COMPUTER JOURNAL, 2024, 67 (08): : 2622 - 2632
  • [37] Ensemble based Active Annotation for Named Entity Recognition
    Ekbal, Asif
    Saha, Sriparna
    Singh, Dhirendra
    2012 THIRD INTERNATIONAL CONFERENCE ON EMERGING APPLICATIONS OF INFORMATION TECHNOLOGY (EAIT), 2012, : 331 - 334
  • [38] LSTM-Based NeuroCRFs for Named Entity Recognition
    Rondeau, Marc-Antoine
    Su, Yi
    17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5: UNDERSTANDING SPEECH PROCESSING IN HUMANS AND MACHINES, 2016, : 665 - 669
  • [39] Named Entity Recognition Based on Span Semantic Enhancement
    Geng R.
    Chen Y.
    Tang R.
    Huang R.
    Qin Y.
    Dong B.
    Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 2022, 56 (07): : 118 - 126
  • [40] Research on Chinese Named Entity Recognition Based on Ontology
    Chang, Weili
    Luo, Fang
    Qian, Jilai
    MECHANICAL ENGINEERING AND INTELLIGENT SYSTEMS, PTS 1 AND 2, 2012, 195-196 : 1180 - 1185