Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (3): 276-281.doi: 10.11896/jsjkx.220200020

• Artificial Intelligence • Previous Articles     Next Articles

Study on Chinese Named Entity Extraction Rules Based on Boundary Location and Correction

LIU Pan1, GUO Yanming1, LEI Jun1, LAO Mingrui2, LI Guohui1   

  1. 1 College of Systems Engineering,National University of Defense Technology,Changsha 410000,China
    2 LIACS Media Lab,Leiden University,Leiden 2333CA,The Netherlands
  • Received:2022-02-01 Revised:2022-05-13 Online:2023-03-15 Published:2023-03-15
  • About author:LIU Pan,born in 1990,postgraduate.His main research interests include na-tural language processing,computer vision and deep learning.
    GUO Yanming,born in 1989,Ph.D,associate professor.His main research interests include computer vision,natural language processing and deep learning.
  • Supported by:
    National Natural Science Foundation of China(61806218,71673293) and Natural Science Foundation of Hunan Province,China(2019JJ50722).

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Abstract: Compared with English text which is naturally composed of words,Chinese text has no word delimiters,so the combination of Chinese characters is more flexible,and it's more difficult to determine the entity boundaries in Chinese named entity recognition(NER).Current mainstream methods transform the NER task into a sequence labeling task.This paper studies the predicted label sequence under the BIOES tag scheme and calculates the entity boundary accuracy by separately considering the entity head label B or tail label E,which shows that increasing the boundary accuracy can further improve the accuracy of entity recognition.We expand the boundaries of entities with continuous labels,use the label type of the last character of the entity to correct the entity type,and use the word segmentation information to fill in the entity with incomplete labels.Finally,this paper proposes a BIO+ES labeling scheme that adds boundary labels to distinguish non-entity characters at entity boundaries and further improves the performance of Chinese NER.

Key words: Chinese named entity recognition, Tag scheme, Entity extraction

CLC Number: 

  • TP391
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