Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 18-23.doi: 10.11896/jsjkx.200500090

• Artificial Intelligence • Previous Articles     Next Articles

Named Entity Recognition in Field of Ancient Chinese Based on Lattice LSTM

CUI Dan-dan, LIU Xiu-lei, CHEN Ruo-yu, LIU Xu-hong, LI Zhen, QI Lin   

  1. Computer School,Beijing Information Science and Technology University,Beijing 100192,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:CUI Dan-dan,born in 1997,postgra-duate.Her main research interests include natural language processing and knowledge graph.
    CHEN Ruo-yu,born in 1982,Ph.D,lecturer,is a member of China Computer Federation.His main research interests include natural language processing,data mining and semantic Web.
  • Supported by:
    This work was supported by the National Key R&D Program of China(2017YFB1400402).

Abstract: Investigated the named entity recognition problem of ancient Chinese literature based on the Complete Collection of Four Treasuries dataset.Proposed an algorithm for named entity recognition of ancient Chinese literature based on the Lattice LSTM model.This method combines both character sequence information and word sequence information as input to the model.Using jiayan word segmentation tool,word2vec is used to train character and word level embedding of ancient Chinese as input to the Lattice LSTM model,which improves the performance of named entity recognition based on ancient Chineseliterature.Based on the Lattice LSTM model and pre-trained character and word level embedding of ancient Chinese,the performance of named entity recognition based on ancient Chinese literature is improved.Compared with the traditional Bi-LSTM-CRF model,its F1 score is improved by about 3.95%.

Key words: Ancient Chinese literature, BiLSTM-CRF, Deep learning, Lattice LSTM, Named entity recognition

CLC Number: 

  • TP312
[1] CHEN S D,OUYANG X Y.Summary of Named Entity Recognition Technology[J].Radio Communication Techology,2020,46(3):251-260.
[2] ZHOU K.Research on Rule-based Named Entity Recognition[D].Hefei:Hefei University of Technology,2010.
[3] BAO M N,SI L,GE L.Research on Mongolian Named Entity Recognition Based on Dictionary Matching[J].Journal of Minzu University of China,2017,44(3):165-169.
[4] CHEN J,CHANG Z Q,XU J.Recognition and Classification of Biomedical Named Entities Based on HMM[J].Computer Age,2006(10):40-42.
[5] WANG H C,ZHAO T J.Recognition of Named Biomedical Entities Based on SVM[J].Journal of Harbin Engineering University,2006,27(z1):570-574.
[6] SHI H F.Research on Chinese Named Entity Recognition Based on CRF[D].Suzhou:Soochow University,2010.
[7] GU Y.Research on Recognition of Complex Chinese Named Entity Based on BiLSTM-CRF[D].Nanjing:Nanjing University,2019.
[8] MAI M T,FU A Y,WU S E,et al.Uyghur Named Entity Recognition Based on BiLSTM-CNN-CRF Model[J].Computer Engineering,2018,44(8):230-236.
[9] ZHANG X T.Research and Implementation of Medical TextChinese NamedEntity Recognition Based on Lattice LSTM[D].University of Electronic Science and Technology of China,2019.
[10] LI Y Y.Research on Named Entity Recognition AlgorithmBased on Attention Mechanism[D].Beijing:Beijing University of Posts and Telecommunications,2019.
[11] SHENG J.Application of transfer learning in named entity recognition[D].Harbin:Harbin Institute of Technology,2019.
[12] KHAN W,DAUD A,ALOTAIBI F,et al.Deep recurrent neural networks with word embeddings for Urdu named entity recognition[J].ETRI Journal,2020,42(1).
[13] KURU O,CAN O A,YURET D.Charner:Character-levelnamed entity recognition[C]//COLING.2016:911-921.
[14] REI M.Semi-supervised multitask learning for sequence labeling[C]//ACL.2017:2121-2130.
[15] TRAN Q,MACKINLAY A,YEPES A J.Named entity recognition with stack residual lstm and trainable bias decoding[C]//IJCNLP.2017:566-575.
[16] YE Z X,LING Z H.Hybrid semi-markovcrf for neural sequence labeling[C]//ACL.2018:235-240.
[17] LAMPLE G,BALLESTEROS M,SUBRAMANIAN S,et al.Neural architectures for named entity recognition[C]//NAACL.2016:260-270.
[18] GREGORIC A Z,BACHRACH Y,COOPE S.Named entity recognition with parallel recurrent neural networks[C]//ACL.2018:69-74.
[19] ZHANG C P,FANG T,LIU Y L.Research and Application of Named Entity Recognition Technology Based on LSTM-CRF[J].Computer Technology and Development,2019,29(2):106-108.
[20] Biotechnology-Biomedical Informatics;Data on Biomedical Informatics Described by Researchers at National University of Defence Science and Technology (Adversarial training based lattice LSTM for Chinese clinical named entity recognition)[J/OL].https://schlr.cnki.net/Detail/index/SPQDLAST/SPQDC4D58041D94430FE02664BB977E1BBDB.
[21] WANG B R,LIN X,ZHU X D,et al.Research on Named Entity Recognition Model of Chinese Medical Text by Lattice LSTM Neural Network[J].Chinese Journal of Health Informatics and Management,2019,16(1):84-88.
[22] ZHAO S,CAI Z P,CHEN H W,et al.Adversarial trainingbased lattice LSTM for Chinese clinical named entity recognition[J].Journal of Biomedical Informatics,2019,99.
[23] ZHANG W J,ZHANG H M,YANG L,et al.Multi-granular Chinese Word Segmentation Based on Lattice-LSTM[J].Journal of Chinese Information Processing,2019,33(1):18-24.
[1] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[2] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[3] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[4] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[5] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[6] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[7] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[8] HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163.
[9] ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169.
[10] SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235.
[11] HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[12] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[13] ZHU Wen-tao, LAN Xian-chao, LUO Huan-lin, YUE Bing, WANG Yang. Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN [J]. Computer Science, 2022, 49(6A): 378-383.
[14] WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
[15] MAO Dian-hui, HUANG Hui-yu, ZHAO Shuang. Study on Automatic Synthetic News Detection Method Complying with Regulatory Compliance [J]. Computer Science, 2022, 49(6A): 523-530.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!