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Chinese named entity recognition in power domain based on Bi-LSTM-CRF

Authors:

Zhenqiang Zhao,

Xiaohui JiAuthors Info & Claims

AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

Pages 176 - 180

https://doi.org/10.1145/3357254.3357283

Published: 16 August 2019 Publication History

Abstract

Efficient recognition of proprietary entities is an important basic work for text data mining and intelligent application in power domain. Traditional power domain Named Entity Recognition (NER) methods rely on feature engineering seriously, which unable to learn power entity features automatically. In order to learn entity features automatically and extract power domain named entities efficiently, a model based on Bidirectional Long Short-Term Memory Neural Networks (Bi-LSTM) and Conditional Random Field (CRF) was proposed in this paper. Word representations were fed into the neural networks as an additional feature and Skip-gram embeddings were trained on power domain corpus. Experimental results showed the precision rate reaches higher than 88.25% and the recalling rate reaches higher than 88.04%, which confirm the method based on Bi-LSTM and CRF is effective for named entity recognition in the power domain.

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Cited By

Liu CZhang QFeng JTian Y(2022)A Chinese word segmentation method based on dictionary and HMMProceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering10.1145/3573428.3573542(644-649)Online publication date: 21-Oct-2022
https://dl.acm.org/doi/10.1145/3573428.3573542
Chen JXu XZhang X(2022)Radial Basis Function Attention for Named Entity RecognitionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/353901422:1(1-18)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1145/3539014
Bi JNi KYuan HQiao J(2022)LBPSC: A Hybrid Prediction Model for Chinese Named Entity Recognition in Water Environment2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53654.2022.9945417(223-228)Online publication date: 9-Oct-2022
https://doi.org/10.1109/SMC53654.2022.9945417
Show More Cited By

Index Terms

Chinese named entity recognition in power domain based on Bi-LSTM-CRF
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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Published In

cover image ACM Other conferences

AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

August 2019

198 pages

ISBN:9781450372299

DOI:10.1145/3357254

Conference Chairs:
Li Ma
North China University of Technology, China
,
Xu Huang
University of Canberra, Australia

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 August 2019

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Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology
Sichuan Science and Technology Program
Science and Technology Program of State Grid Corporation of China
Science and Technology Innovation Program of China Electric Power Research Institute

Conference

AIPR 2019

AIPR 2019: 2019 2nd International Conference on Artificial Intelligence and Pattern Recognition

August 16 - 18, 2019

Beijing, China

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Cited By

Liu CZhang QFeng JTian Y(2022)A Chinese word segmentation method based on dictionary and HMMProceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering10.1145/3573428.3573542(644-649)Online publication date: 21-Oct-2022
https://dl.acm.org/doi/10.1145/3573428.3573542
Chen JXu XZhang X(2022)Radial Basis Function Attention for Named Entity RecognitionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/353901422:1(1-18)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1145/3539014
Bi JNi KYuan HQiao J(2022)LBPSC: A Hybrid Prediction Model for Chinese Named Entity Recognition in Water Environment2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53654.2022.9945417(223-228)Online publication date: 9-Oct-2022
https://doi.org/10.1109/SMC53654.2022.9945417
Zheng KSun LWang XZhou SLi HLi SZeng LGong Q(2021)Named Entity Recognition in Electric Power Metering Domain Based on Attention MechanismIEEE Access10.1109/ACCESS.2021.31231549(152564-152573)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3123154

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