An Event Extraction Method for Power Dispatching Text Based on Hybrid Neural Network

doi:10.11930/j.issn.1004-9649.202111096

Abstract

Abstract: In order to improve the application effect of power dispatching text in business scenarios, a method for extracting power dispatching text events is proposed based on hybrid neural network. Taking the power dispatching text fault handling plan as the research object, an event extraction model is established centering on the trigger words of the plan. It is proved through case simulation that the proposed method can better extract the entity elements in the plan event. Compared with other methods, the accuracy of the entity recognition and entity relation recognition is improved.

Key words: power grid dispatching, hybrid neural network, trigger word, event extraction

LIU He, PI Junbo, SONG Pengcheng, ZHAO Hanlin, ZHANG Yue, LIU Xianzhuang. An Event Extraction Method for Power Dispatching Text Based on Hybrid Neural Network[J]. Electric Power, 2022, 55(9): 105-110,120.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202111096

https://www.electricpower.com.cn/EN/Y2022/V55/I9/105

References

[1] 蒲天骄, 谈元鹏, 彭国政, 等. 电力领域知识图谱的构建与应用[J]. 电网技术, 2021, 45(6): 2080–2091
PU Tianjiao, TAN Yuanpeng, PENG Guozheng, et al. Construction and application of knowledge graph in the electric power field[J]. Power System Technology, 2021, 45(6): 2080–2091
[2] 张晓华, 冯长有, 王永明, 等. 电网调控机器人设计思路[J]. 电力系统自动化, 2019, 43(13): 1–8
ZHANG Xiaohua, FENG Changyou, WANG Yongming, et al. Design ideas of robotic dispatcher for power grid[J]. Automation of Electric Power Systems, 2019, 43(13): 1–8
[3] 郑伟彦, 杨勇, 卢家驹, 等. 面向配电网知识图谱的配电调度文本实体链接方法[J]. 电力系统保护与控制, 2021, 49(4): 111–117
ZHENG Weiyan, YANG Yong, LU Jiaju, et al. Entity linking method of distribution dispatching texts for a distribution network knowledge graph[J]. Power System Protection and Control, 2021, 49(4): 111–117
[4] 余建明, 王小海, 张越, 等. 面向智能调控领域的知识图谱构建与应用[J]. 电力系统保护与控制, 2020, 48(3): 29–35
YU Jianming, WANG Xiaohai, ZHANG Yue, et al. Construction and application of knowledge graph for intelligent dispatching and control[J]. Power System Protection and Control, 2020, 48(3): 29–35
[5] 鲁华永, 袁越, 郭泓佐, 等. 基于正则表达式的变电站集中监控信息解析方法[J]. 电力系统自动化, 2017, 41(5): 78–83
LU Huayong, YUAN Yue, GUO Hongzuo, et al. Regular expressions based information analytic method for substation centralized monitoring[J]. Automation of Electric Power Systems, 2017, 41(5): 78–83
[6] 杨锦锋, 于秋滨, 关毅, 等. 电子病历命名实体识别和实体关系抽取研究综述[J]. 自动化学报, 2014, 40(8): 1537–1562
YANG Jinfeng, YU Qiubin, GUAN Yi, et al. An overview of research on electronic medical record oriented named entity recognition and entity relation extraction[J]. Acta Automatica Sinica, 2014, 40(8): 1537–1562
[7] 李慧林, 柴玉梅, 孙穆祯. 面向文本命名实体识别的深层网络模型[J]. 小型微型计算机系统, 2019, 40(1): 50–57
LI Huilin, CHAI Yumei, SUN Muzhen. Deep network model for text named entity recognition[J]. Journal of Chinese Computer Systems, 2019, 40(1): 50–57
[8] WILLIAMS R J, ZIPSER D. A learning algorithm for continually running fully recurrent neural networks[J]. Neural Computation, 1989, 1(2): 270–280.
[9] 王朱君, 王石, 李雪晴, 等. 基于深度学习的事件因果关系抽取综述[J]. 计算机应用, 2021, 41(5): 1247–1255
WANG Zhujun, WANG Shi, LI Xueqing, et al. Review of event causality extraction based on deep learning[J]. Journal of Computer Applications, 2021, 41(5): 1247–1255
[10] 陈斌, 周勇, 刘兵. 基于卷积双向长短期记忆网络的事件触发词抽取[J]. 计算机工程, 2019, 45(1): 153–158
CHEN Bin, ZHOU Yong, LIU Bing. Event trigger word extraction based on convolutional bidirectional long short term memory network[J]. Computer Engineering, 2019, 45(1): 153–158
[11] 吴文涛, 李培峰, 朱巧明. 基于混合神经网络的实体和事件联合抽取方法[J]. 中文信息学报, 2019, 33(8): 77–83
WU Wentao, LI Peifeng, ZHU Qiaoming. Joint extraction of entities and events by a hybrid neural network[J]. Journal of Chinese Information Processing, 2019, 33(8): 77–83
[12] 潘璋, 黄德根. 事件要素注意力与编码层融合的触发词抽取研究[J]. 小型微型计算机系统, 2021, 42(4): 673–677
PAN Zhang, HUANG Degen. Research on trigger word extraction based on the fusion of event argument attention and encoder layer[J]. Journal of Chinese Computer Systems, 2021, 42(4): 673–677
[13] 李明节, 陶洪铸, 许洪强, 等. 电网调控领域人工智能技术框架与应用展望[J]. 电网技术, 2020, 44(2): 393–400
LI Mingjie, TAO Hongzhu, XU Hongqiang, et al. The technical framework and application prospect of artificial intelligence application in the field of power grid dispatching and control[J]. Power System Technology, 2020, 44(2): 393–400
[14] 陶洪铸, 翟明玉, 许洪强, 等. 适应调控领域应用场景的人工智能平台体系架构及关键技术[J]. 电网技术, 2020, 44(2): 412–419
TAO Hongzhu, ZHAI Mingyu, XU Hongqiang, et al. Architecture and key technologies of artificial intelligence platform oriented for power grid dispatching and control application scenarios[J]. Power System Technology, 2020, 44(2): 412–419
[15] 王福贺, 海威, 张越, 等. 电网线路故障处置智能调度机器人研究及应用[J]. 电气自动化, 2021, 43(3): 1–3,23
WANG Fuhe, HAI Wei, ZHANG Yue, et al. Research and application of intelligent dispatching robots handling grid line faults[J]. Electrical Automation, 2021, 43(3): 1–3,23
[16] 佟佳弘, 武志刚, 管霖, 等. 电力调度文本的自然语言理解与解析技术及应用[J]. 电网技术, 2020, 44(11): 4148–4156
TONG Jiahong, WU Zhigang, GUAN Lin, et al. Power dispatching text analysis and application based on natural language understanding[J]. Power System Technology, 2020, 44(11): 4148–4156
[17] 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
JIANG Chen, WANG Yuan, HU Junhua, et al. Power entity information recognition based on deep learning[J]. Power System Technology, 2021, 45(6): 2141–2149
[18] 丁禹, 尚学伟, 米为民. 基于深度学习的电网调控文本知识抽取方法[J]. 电力系统自动化, 2020, 44(24): 161–168
DING Yu, SHANG Xuewei, MI Weimin. Deep learning based knowledge extraction method for text of power grid dispatch and control[J]. Automation of Electric Power Systems, 2020, 44(24): 161–168
[19] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[EB/OL]. https://arxiv.org/abs/1810.04805
[20] 田园, 原野, 刘海斌, 等. 基于BERT预训练语言模型的电网设备缺陷文本分类[J]. 南京理工大学学报, 2020, 44(4): 446–453
TIAN Yuan, YUAN Ye, LIU Haibin, et al. BERT pre-trained language model for defective text classification of power grid equipment[J]. Journal of Nanjing University of Science and Technology, 2020, 44(4): 446–453
[21] ZHANG W T, JIANG S H, ZHAO S, et al. A BERT-BiLSTM-CRF model for Chinese electronic medical records named entity recognition[C]//2019 12 th International Conference on Intelligent Computation Technology and Automation (ICICTA). Xiangtan, China. IEEE, 2019: 166-169.
[22] 叶远波, 李端超, 谢民, 等. 基于知识图谱的二次设备测试自动配置方法[J]. 电力系统保护与控制, 2022, 50(12): 162–171
YE Yuanbo, LI Duanchao, XIE Min, et al. Automatic configuration method of secondary equipment test based on a knowledge graph[J]. Power System Protection and Control, 2022, 50(12): 162–171
[23] KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar. Stroudsburg, PA, USA: Association for Computational Linguistics, 2014: 1746–1751.
[24] 李志杰, 耿朝阳, 宋鹏. LSTM-TextCNN联合模型的短文本分类研究[J]. 西安工业大学学报, 2020, 40(3): 299–304
LI Zhijie, GENG Chaoyang, SONG Peng. Research on short text classification based on joint LSTM TextCNN model[J]. Journal of Xi'an Technological University, 2020, 40(3): 299–304
[25] ZHANG Q, ZHENG R R, ZHAO Z Y, et al. A TextCNN based approach for multi-label text classification of power fault data[C]//2020 IEEE 5 th International Conference on Cloud Computing and Big Data Analytics. Chengdu, China. IEEE, 2020: 179–183