计算机科学 ›› 2024, Vol. 51 ›› Issue (11A): 240300148-6.doi: 10.11896/jsjkx.240300148

• 智能计算 • 上一篇    下一篇

基于BiLSTM-CRF的航行通告命名实体识别研究

项恒, 杨明友, 李猛   

  1. 中国民航大学空中交通管理学院 天津 300300
  • 出版日期:2024-11-16 发布日期:2024-11-13
  • 通讯作者: 项恒(5136849@qq.com)
  • 基金资助:
    国家自然科学基金(U1833103).

Study on Named Entity Recognition of NOTAM Based on BiLSTM-CRF

XIANG Heng, YANG Mingyou, LI Meng   

  1. College of Air Traffic Management,Civil Aviation University of China,Tianjin 300300,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:XIANG Heng,born in 1975,master,associate professor.His main research interests include air traffic control human factors and aeronautical information management.
  • Supported by:
    National Natural Science Foundation of China(U1833103).

摘要: 针对当前国际民航组织对数字航行通告研究仅考虑对文本航行通告环境兼容,而未考虑对数字航行通告环境兼容的问题,提出一种基于BiLSTM-CRF的航行通告命名实体识别模型,以实现文本航行通告中相关实体的自动识别,并为转换数字航行通告提供所需的基本数据。通过构建航行通告语料标记数据集对LSTM,BiLSTM,BiLSTM-CRF 3种模型进行对比实验。实验结果显示,所提模型的精确率、召回率、F1值分别为95%,95%,95%,验证了其在航行通告领域的有效性,证明本研究可以有效识别航行通告中的重要实体信息。

关键词: 航行通告, 命名实体识别, 深度学习, 双向长短期记忆网路, 条件随机场

Abstract: Aiming at the problem that the current research of International Civil Aviation Organization in digital NOTAMs,which only considers the compatibility with the environment of textual NOTAMs,but not digital NOTAMs,a named entity recognition model for NOTAMs based on BiLSTM-CRF is proposed to realise the automatic recognition of relevant entities in textual NOTAMs and to provide the necessary basic data for the conversion of digital NOTAMs.Comparative experiments are carried out by constructing a NOTAM corpus tagged dataset in three models,LSTM,BiLSTM and BiLSTM-CRF,and the experimental results show that the precision,recall and F1 value of the proposed method is 95%,95% and 95%,respectively,which verifies the effectiveness of the proposed method in the field of NOTAMs and proves that this study can effectively obtain the important entity information in NOTAMs.

Key words: NOTAM, Name entity recognition, Deep learning, BiLSTM, CRF

中图分类号: 

  • V355
[1]CAAC.Specification for the Preparation and Issuance of CivilAviation NOTAMS [Z].2011:30P.;A4.
[2]EUROCONTROL.Digital NOTAM Specification[EB/OL].[2024-03-15].https://ext.eurocontrol.int/aixm_confluence/display/DNOTAM/Digital+NOTAM+Specification.
[3]GUO X Y,HE T T.Survey about Research on Information Extraction [J].Computer Science,2015,42(2):14-17,38.
[4]GAO X,WANG S,ZHU J W,et al.Overview of Named Entity Recognition Tasks [J].Computer Science,2023,50(S1):26-33.
[5]RABINER L R.A tutorial on hidden Markov models and selec-ted applications in speech recognition [J].Proc IEEE,1989,77.
[6]PIETRA S D,PIETRA V D,MERCER R L,et al.Adaptive language modeling using minimum discriminant estimation[C]//IEEE International Conference on Acoustics.IEEE,1992:633-636.
[7]LAFFERTY J,MCCALLUM A,PEREIRA F C N.Conditional Random Fields:Probabilistic Models for Segmenting and Labeling Sequence Data[C]//Proceedings of the 18th International Conference on Machine Learning(ICML 2001).2001:282-289.
[8]CHEN P H,LIN C J,SCHLKOPF B.A Tutorial on v-support vector machines [J].Applied Stochastic Models in Business and Industry,2005,21(2):111-136.
[9]BIKEL D M,SCHWARTZ R,WEISCHEDEL R M.An Algorithm that Learns What's in a Name [J].Machine Learning,1999,34:211-231.
[10]BORTHWICK A,STERLING J,AGICHTEIN E,et al.Description of the MENE named entity system as used in MUC-7[C]//Proceedings of the 7th Message Understanding Conference,Virginia.Stroudsburg:ACL,1998:1-7.
[11]MCCALLUM A,LI W.Early Results for Named Entity Recognition with Conditional Random Fields,Feature Induction and Web-Enhanced Lexicons [J].Association for Computational Linguistics,2003,4:188-191.
[12]TAKEUCHI K,COLLIER N.Use of Support vector machinesin extended named entity recognition[C]//Proceedings of the 6th Conference on Natural Language Learning,Taipei,China,Aug 24-Sep 1,2002.Stroudsburg:ACL,2002:184-190.
[13]COLLOBERT R,WESTON J,BOTTOU L,et al.Natural Language Processing(almost) from Scratch [J].Journal of Machine Learning Research,2011,12(1):2493-2537.
[14]HUANG Z,XU W,YU K.Bidirectional LSTM-CRF Models forSequence Tagging [J].arXiv:1508.01991,2015.
[15]CETOLI A,BRAGAGLIA S,O'HARNEY A D,et al.Graph convolutional networks for named entity recognition[C]//Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories.Stroudsburg:ACL,2018:37-45.
[16]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding [C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long and Short Papers).2019:4171-4186.
[17]GUO Z X,DENG X L.Intelligent Identification Method of LegalCase Entity Based on BERT-BiLSTM-CRF [J].Journal of Beijing University of Posts and Telecommunications,2021,44(4):129-134.
[18]LIU H,ZHANG Z X,WANG Y F.A Review on Main Optimization Methods of BERT [J].Data Analysis and Knowledge Discovery,2021,5(1):3-15.
[19]LI H F,ZENG C,HU H Q,etal.Research on Text Classification of NOTAM Based on Machine Learning [J].Journal of Ci-vil Aviation,2022,6(4):6-9.
[20]XIANG H,ZHANG C,LI M.Identification method for non-normative NOTAM based on NLP [J].Journal of Civil Aviation University of China,2022,40(2):14-18.
[21]PAN Z X,LUO Y H,LI RZ.Research on Extraction of Notam Information [J].Modern Computer,2022,28(2):82-87.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!