Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (12): 3711-3718.DOI: 10.11772/j.issn.1001-9081.2022121897
• Artificial intelligence • Previous Articles Next Articles
Fei XIA1, Shuaiqi CHEN1, Min HUA2(), Bihong JIANG3
Received:
2022-12-26
Revised:
2023-02-26
Accepted:
2023-03-02
Online:
2023-04-27
Published:
2023-12-10
Contact:
Min HUA
About author:
XIA Fei, born in 1978, Ph. D., associate professor. His research interests include power data analysis, power image processing.Supported by:
通讯作者:
华珉
作者简介:
夏飞(1978—),男,江西南昌人,副教授,博士,CCF高级会员,主要研究方向:电力数据分析、电力图像处理基金资助:
CLC Number:
Fei XIA, Shuaiqi CHEN, Min HUA, Bihong JIANG. Chinese word segmentation method in electric power domain based on improved BERT[J]. Journal of Computer Applications, 2023, 43(12): 3711-3718.
夏飞, 陈帅琦, 华珉, 蒋碧鸿. 基于改进BERT的电力领域中文分词方法[J]. 《计算机应用》唯一官方网站, 2023, 43(12): 3711-3718.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022121897
优化器 | ||
---|---|---|
SGD | ||
Adam |
Tab.1 Values of α and β corresponding to SGD and Adam optimizers
优化器 | ||
---|---|---|
SGD | ||
Adam |
语料库名称 | 语料库汉字数 |
---|---|
MSRA | 336 722 |
PKU | 339 406 |
电力领域专利文本语料库 | 338 173 |
Tab.2 Composition of training corpus
语料库名称 | 语料库汉字数 |
---|---|
MSRA | 336 722 |
PKU | 339 406 |
电力领域专利文本语料库 | 338 173 |
BERT编码器层数 | F1/% | 每代训练时间/s | BERT编码器层数 | F1/% | 每代训练时间/s |
---|---|---|---|---|---|
15 | 91.16 | 2 731 | 40 | 92.87 | 3 936 |
20 | 91.52 | 2 957 | 45 | 92.88 | 4 324 |
25 | 92.21 | 3 120 | 50 | 92.88 | 4 867 |
30 | 92.53 | 3 430 | 55 | 92.89 | 5 638 |
35 | 92.76 | 3 689 | 60 | 92.90 | 6 560 |
Tab.3 Test results of model performance and time consumption
BERT编码器层数 | F1/% | 每代训练时间/s | BERT编码器层数 | F1/% | 每代训练时间/s |
---|---|---|---|---|---|
15 | 91.16 | 2 731 | 40 | 92.87 | 3 936 |
20 | 91.52 | 2 957 | 45 | 92.88 | 4 324 |
25 | 92.21 | 3 120 | 50 | 92.88 | 4 867 |
30 | 92.53 | 3 430 | 55 | 92.89 | 5 638 |
35 | 92.76 | 3 689 | 60 | 92.90 | 6 560 |
分词模型 | P | R | F1 |
---|---|---|---|
HMM[ | 77.41 | 78.94 | 78.17 |
METASEG[ | 82.20 | 83.76 | 82.98 |
ZEN[ | 86.16 | 87.70 | 86.92 |
LEBERT[ | 88.21 | 90.35 | 89.27 |
本文模型 | 93.12 | 92.62 | 92.87 |
Tab.4 Test results of word segmentation by different models
分词模型 | P | R | F1 |
---|---|---|---|
HMM[ | 77.41 | 78.94 | 78.17 |
METASEG[ | 82.20 | 83.76 | 82.98 |
ZEN[ | 86.16 | 87.70 | 86.92 |
LEBERT[ | 88.21 | 90.35 | 89.27 |
本文模型 | 93.12 | 92.62 | 92.87 |
分词模型 | 分词结果 |
---|---|
HMM | 例1:安装/于/超高压/输电/线路/架空/地线/上 |
例2:柔性/再生/碳纤维/的/纤维素/基/电磁/屏蔽/薄膜 | |
例3:在/一个/所述/区域/能量/云内/,/或是/至少/两个/ 所述/区域/能量/云间/进行/能源/交互 | |
例4:涉及/一种/风雨/水能/发电/装置 | |
METASEG | 例1:安装/于/超高压/输电线路/架空/地线/上 |
例2:柔性/再生/碳纤维/的/纤维素/基/电磁屏蔽/薄膜 | |
例3:在/一个/所述/区域/能量云/内/,/或/是/至少/两个/ 所述/区域/能量/云间/进行/能源/交互 | |
例4:涉及/一种/风雨/水能发电/装置 | |
ZEN | 例1:安装/于/超高压/输电/线路/架空/地线/上 |
例2:柔性/再生/碳纤维/的/纤维素/基/电磁/屏蔽/薄膜 | |
例3:在/一个/所述/区域/能量/云/内/,/或/是/至少/两个/ 所述/区域/能量/云间/进行/能源/交互 | |
例4:涉及/一种/风雨/水能/发电/装置 | |
LEBERT | 例1:安装/于/超高压/输电/线路/架空/地线/上 |
例2:柔性/再生/碳纤维/的/纤维素基/电磁/屏蔽/薄膜 | |
例3:在/一个/所述/区域/能量云/内/,/或/是/至少/两个/ 所述/区域/能量云/间/进行/能源/交互 | |
例4:涉及/一种/风雨/水能/发电/装置 | |
本文模型 | 例1:安装/于/超高压/输电线路/架空/地线/上 |
例2:柔性/再生/碳纤维/的/纤维素基/电磁屏蔽/薄膜 | |
例3:在/一个/所述/区域/能量云/内/,/或/是/至少/两个/ 所述/区域/能量云/间/进行/能源交互 | |
例4:涉及/一种/风雨水能/发电/装置 |
Tab.5 Examples of word segmentation results by different models
分词模型 | 分词结果 |
---|---|
HMM | 例1:安装/于/超高压/输电/线路/架空/地线/上 |
例2:柔性/再生/碳纤维/的/纤维素/基/电磁/屏蔽/薄膜 | |
例3:在/一个/所述/区域/能量/云内/,/或是/至少/两个/ 所述/区域/能量/云间/进行/能源/交互 | |
例4:涉及/一种/风雨/水能/发电/装置 | |
METASEG | 例1:安装/于/超高压/输电线路/架空/地线/上 |
例2:柔性/再生/碳纤维/的/纤维素/基/电磁屏蔽/薄膜 | |
例3:在/一个/所述/区域/能量云/内/,/或/是/至少/两个/ 所述/区域/能量/云间/进行/能源/交互 | |
例4:涉及/一种/风雨/水能发电/装置 | |
ZEN | 例1:安装/于/超高压/输电/线路/架空/地线/上 |
例2:柔性/再生/碳纤维/的/纤维素/基/电磁/屏蔽/薄膜 | |
例3:在/一个/所述/区域/能量/云/内/,/或/是/至少/两个/ 所述/区域/能量/云间/进行/能源/交互 | |
例4:涉及/一种/风雨/水能/发电/装置 | |
LEBERT | 例1:安装/于/超高压/输电/线路/架空/地线/上 |
例2:柔性/再生/碳纤维/的/纤维素基/电磁/屏蔽/薄膜 | |
例3:在/一个/所述/区域/能量云/内/,/或/是/至少/两个/ 所述/区域/能量云/间/进行/能源/交互 | |
例4:涉及/一种/风雨/水能/发电/装置 | |
本文模型 | 例1:安装/于/超高压/输电线路/架空/地线/上 |
例2:柔性/再生/碳纤维/的/纤维素基/电磁屏蔽/薄膜 | |
例3:在/一个/所述/区域/能量云/内/,/或/是/至少/两个/ 所述/区域/能量云/间/进行/能源交互 | |
例4:涉及/一种/风雨水能/发电/装置 |
分词模型 | batch_size | 每代训练时间/s | 显存占用/MB | F1/% |
---|---|---|---|---|
非PSAttn模型 | 1 | 5 451 | 8 472 | 92.81 |
2 | 2 938 | 11 381 | 92.63 | |
3 | 1 987 | 14 315 | 92.48 | |
PSAttn模型 | 1 | 3 936 | 6 258 | 92.87 |
2 | 1 980 | 8 892 | 92.71 | |
3 | 1 198 | 11 536 | 92.73 | |
4 | 903 | 14 174 | 92.44 |
Tab.6 Test results of training speed and memory consumption
分词模型 | batch_size | 每代训练时间/s | 显存占用/MB | F1/% |
---|---|---|---|---|
非PSAttn模型 | 1 | 5 451 | 8 472 | 92.81 |
2 | 2 938 | 11 381 | 92.63 | |
3 | 1 987 | 14 315 | 92.48 | |
PSAttn模型 | 1 | 3 936 | 6 258 | 92.87 |
2 | 1 980 | 8 892 | 92.71 | |
3 | 1 198 | 11 536 | 92.73 | |
4 | 903 | 14 174 | 92.44 |
1 | 李刚,李银强,王洪涛,等.电力设备健康管理知识图谱:基本概念、关键技术及研究进展[J].电力系统自动化,2022,46(3):1-13. 10.7500/AEPS20210804001 |
LI G, LI Y Q, WANG H T, et al. Knowledge graph of power equipment health management: basic concepts, key technologies and research progress [J]. Automation of Electric Power Systems, 2022, 46(3): 1-13. 10.7500/AEPS20210804001 | |
2 | 冯斌,张又文,唐昕,等.基于BiLSTM-Attention神经网络的电力设备缺陷文本挖掘[J].中国电机工程学报,2020,40(S1):1-10. 10.13334/j.0258-8013.pcsee.200530 |
FENG B, ZHANG Y W, TANG X, et al. Power equipment defect record text mining based on BiLSTM-attention neural network [J]. Proceedings of the CSEE, 2020, 40(S1): 1-10. 10.13334/j.0258-8013.pcsee.200530 | |
3 | 许尧,马欢,许旵鹏,等.智能变电站继电保护智能运维系统自动配置技术研究[J].电力系统保护与控制,2022,50(11):160-168. |
XU Y, MA H, XU C P, et al. Self-configuration technology of an intelligent operation and maintenance system of intelligent substation relay protection [J]. Power System Protection and Control, 2022, 50(11): 160-168. | |
4 | 唐琳,郭崇慧,陈静锋.中文分词技术研究综述[J].数据分析与知识发现,2020,4(Z1):1-17. |
TANG L, GUO C H, CHEN J F. Review of Chinese word segmentation studies [J]. Data Analysis and Knowledge Discovery, 2020, 4(Z1): 1-17. | |
5 | 钱智勇,周建忠,童国平,等.基于HMM的楚辞自动分词标注研究[J].图书情报工作,2014, 58(4): 105-110. 10.13266/j.issn.0252-3116.2014.04.017 |
QIAN Z Y, ZHOU J Z, TONG G P, et al. Research on automatic word segmentation and pos tagging for Chu Ci based on HMM [J]. Library and Information Service, 2014, 58(4): 105-110. 10.13266/j.issn.0252-3116.2014.04.017 | |
6 | 朱艳辉,刘璟,徐叶强,等.基于条件随机场的中文领域分词研究[J].计算机工程与应用,2016,52(15):97-100. 10.3778/j.issn.1002-8331.1512-0299 |
ZHU Y H, LIU J, XU Y Q, et al. Chinese word segmentation research based on conditional random field [J]. Computer Engineering and Applications, 2016, 52(15): 97-100. 10.3778/j.issn.1002-8331.1512-0299 | |
7 | CHEN X, QIU X, ZHU C, et al. Long short-term memory neural networks for Chinese word segmentation [C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2015: 1197-1206. 10.18653/v1/d15-1141 |
8 | 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). Stroudsburg, PA: Association for Computational Linguistics, 2019: 4171-4186. 10.18653/v1/n18-2 |
9 | SHEIKH I, ILLINA I, FOHR D, et al. OOV proper name retrieval using topic and lexical context models [C]// Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2015: 5291-5295. 10.1109/icassp.2015.7178981 |
10 | ZHANG Q, LIU X, FU J. Neural networks incorporating dictionaries for Chinese word segmentation [C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 5682-5689. 10.1609/aaai.v32i1.11959 |
11 | 宫法明,朱朋海.基于自适应隐马尔可夫模型的石油领域文档分词[J].计算机科学,2018,45(6A):97-100. 10.11896/j.issn.1002-137X.2018.Z6.019 |
GONG F M, ZHU P H. Word segmentation based on adaptive hidden Markov model in oilfield [J]. Computer Science, 2018, 45(6A): 97-100. 10.11896/j.issn.1002-137X.2018.Z6.019 | |
12 | ZHAO L J, ZHANG Q, WANG P, et al. Neural networks incorporating unlabeled and partially-labeled data for cross-domain Chinese word segmentation [C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 4602-4608. 10.24963/ijcai.2018/640 |
13 | 成于思,施云涛.基于深度学习和迁移学习的领域自适应中文分词[J].中文信息学报,2019,33(9):9-16,23. 10.3969/j.issn.1003-0077.2019.09.002 |
CHENG Y S, SHI Y T. Domain adaption of Chinese word segmentation based on deep learning and transfer learning [J]. Journal of Chinese Information Processing, 2019, 33(9): 9-16,23. 10.3969/j.issn.1003-0077.2019.09.002 | |
14 | 崔志远,赵尔平,雒伟群,等.面向专业领域的多头注意力中文分词模型——以西藏畜牧业为例[J].中文信息学报,2021,35(7):72-80. 10.3969/j.issn.1003-0077.2021.07.009 |
CUI Z Y, ZHAO E P, LUO W Q, et al. Multi-head attention for domain specific Chinese word segmentation model — a case study on Tibet’s animal husbandry [J]. Journal of Chinese Information Processing, 2021, 35(7): 72-80. 10.3969/j.issn.1003-0077.2021.07.009 | |
15 | 张军,赖志鹏,李学,等.基于新词发现的跨领域中文分词方法[J].电子与信息学报,2022,44(9):3241-3248. 10.11999/JEIT210675 |
ZHANG J, LAI Z P, LI X, et al. Cross-domain Chinese word segmentation based on new word discovery [J]. Journal of Electronics & Information Technology, 2022, 44(9): 3241-3248. 10.11999/JEIT210675 | |
16 | 刘梓权,王慧芳.基于知识图谱技术的电力设备缺陷记录检索方法[J].电力系统自动化,2018,42(14):158-164. 10.7500/AEPS20180103007 |
LIU Z Q, WANG H F. Retrieval method for defect records of power equipment based on knowledge graph technology [J]. Automation of Electric Power Systems, 2018, 42(14): 158-164. 10.7500/AEPS20180103007 | |
17 | 杜修明,秦佳峰,郭诗瑶,等.电力设备典型故障案例的文本挖掘[J].高电压技术,2018,44(4):1078-1084. 10.13336/j.1003-6520.hve.20180329005 |
DU X M, QIN J F, GUO S Y, et al. Text mining of typical defects in power equipment [J]. High Voltage Engineering, 2018, 44(4): 1078-1084. 10.13336/j.1003-6520.hve.20180329005 | |
18 | 刘荫,张凯,王惠剑,等.面向电力低资源领域的无监督命名实体识别方法[J].中文信息学报,2022,36(6):69-79. 10.3969/j.issn.1003-0077.2022.06.007 |
LIU Y, ZHANG K, WANG H J, et al. Unsupervised low-resource name entities recognition in electric power domain [J]. Journal of Chinese Information Processing, 2022, 36(6): 69-79. 10.3969/j.issn.1003-0077.2022.06.007 | |
19 | 刘文松,胡竹青,张锦辉,等.基于文本特征增强的电力命名实体识别[J].电力系统自动化,2022,46(21):134-142. 10.7500/AEPS20210323003 |
LIU W S, HU Z Q, ZHANG J H, et al. Named entity recognition for electric power industry based on enhanced text features [J]. Automation of Electric Power Systems, 2022, 46(21): 134-142. 10.7500/AEPS20210323003 | |
20 | 蒋晨,王渊,胡俊华,等.基于深度学习的电力实体信息识别方法[J].电网技术,2021,45(6):2141-2149. 10.13335/j.1000-3673.pst.2020.1678 |
JIANG C, WANG Y, HU J H, et al. Power entity information recognition based on deep learning [J]. Power System Technology, 2021, 45(6): 2141-2149. 10.13335/j.1000-3673.pst.2020.1678 | |
21 | 田嘉鹏,宋辉,陈立帆,等.面向知识图谱构建的设备故障文本实体识别方法[J].电网技术,2022,46(10):3913-3922. |
TIAN J P, SONG H, CHEN L F, et al. Entity recognition approach of equipment failure text for knowledge graph construction [J]. Power System Technology, 2022, 46(10): 3913-3922. | |
22 | LIU W, FU X, ZHANG Y, et al. Lexicon enhanced Chinese sequence labeling using BERT adapter [C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2021: 5847-5858. 10.18653/v1/2021.acl-long.454 |
23 | WANG H, MA S, DONG L, et al. DeepNet: scaling Transformers to 1,000 layers [EB/OL]. (2022-03-01) [2022-03-23]. . |
24 | ZHOU H, ZHANG S, PENG J, et al. Informer: beyond efficient Transformer for long sequence time-series forecasting [C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2021: 11106-11115. 10.1609/aaai.v35i12.17325 |
25 | SONG Y, SHI S, LI J, et al. Directional skip-gram: explicitly distinguishing left and right context for word embeddings [C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers). Stroudsburg, PA: Association for Computational Linguistics, 2018: 175-180. 10.18653/v1/n18-2028 |
26 | 国家发展和改革委员会. 电力行业词汇: [S].北京:中国电力出版社,2007:1-20. |
National Development and Reform Commission. Electric power standard thesaurus: [S]. Beijing: China Electric Power Press, 2007:1-20. | |
27 | 杨善让,赵晓彤,杨绍胤.英汉电力技术词典[M].2版.北京:中国电力出版社,2014:1-1469. |
YANG S R, ZHAO X T, YANG S Y. An English-Chinese Dictionary of Electric Power Technology [M]. 2nd ed tion. Beijing: China Electric Power Press, 2014: 1-1469. | |
28 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Proceedings of the 31st Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017: 6000-6010. |
29 | EMERSON T. The second international Chinese word segmentation bakeoff[C]// Proceedings of the 4th SIGHAN Workshop on Chinese Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2005:123-133. |
30 | 俞士汶,段慧明,朱学锋,等.北京大学现代汉语语料库基本加工规范[J].中文信息学报,2002,16(5):49-64. 10.3969/j.issn.1003-0077.2002.05.008 |
YU S W, DUAN H M, ZHU X F, et al. The basic processing of contemporary Chinese corpus at Peking University SPECIFICATION [J]. Journal of Chinese Information Processing, 2002, 16(5): 49-64. 10.3969/j.issn.1003-0077.2002.05.008 | |
31 | HUGGINGFACE. Transformers [CP/OL]. [2021-12-11]. . |
32 | 蒋卫丽,陈振华,邵党国,等.基于领域词典的动态规划分词算法[J].南京理工大学学报,2019,43(1):63-71. |
JIANG W L, CHEN Z H, SHAO D G, et al. Dynamic programming word segmentation algorithm based on domain dictionaries [J]. Journal of Nanjing University of Science and Technology, 2019, 43(1): 63-71. | |
33 | KE Z, SHI L, SUN S T, et al. Pre-training with meta learning for Chinese word segmentation [C]// Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA: Association for Computational Linguistics, 2021: 5514-5523. 10.18653/v1/2021.naacl-main.436 |
34 | DIAO S, BAI J, SONG Y, et al. ZEN: pre-training Chinese text encoder enhanced by n-gram representations [C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2020: 4729-4740. 10.18653/v1/2020.findings-emnlp.425 |
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