面向煤矿的实体识别与关系抽取模型

doi:10.11772/j.issn.1001-9081.2019122255

计算机应用 ›› 2020, Vol. 40 ›› Issue (8): 2182-2188.DOI: 10.11772/j.issn.1001-9081.2019122255

面向煤矿的实体识别与关系抽取模型

张心怡^1,2,3, 冯仕民^1,2,3, 丁恩杰^1,2,3

1. 矿山互联网应用技术国家地方联合工程实验室(中国矿业大学), 江苏徐州 221008;
2. 中国矿业大学信息与控制工程学院, 江苏徐州 221008;
3. 中国矿业大学物联网(感知矿山)研究中心, 江苏徐州 221008

收稿日期:2020-01-09 修回日期:2020-04-24 出版日期:2020-08-10 发布日期:2020-05-14
通讯作者: 冯仕民(1983-),男,江苏徐州人,研究员,博士,主要研究方向:矿山物联网、多传感器智能信息融合及应用;879151468@qq.com
作者简介:张心怡(1995-),女,陕西西安人,硕士研究生,主要研究方向:矿山物联网、矿工不安全行为识别方法及应用;丁恩杰(1962-),男,江苏徐州人,教授,博士,主要研究方向:矿山物联网。
基金资助:
国家重点研发计划项目（2017YFC0804401）。

Entity recognition and relation extraction model for coal mine

ZHANG Xinyi^1,2,3, FENG Shimin^1,2,3, DING Enjie^1,2,3

1. The National Joint Engineering Laboratory of Internet Applied Technology of Mines(China University of Mining and Technology), Xuzhou Jiangsu 221008, China;
2. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221008, China;
3. IoT Perception Mine Research Center, China University of Mining and Technology, Xuzhou Jiangsu 221008, China

Received:2020-01-09 Revised:2020-04-24 Online:2020-08-10 Published:2020-05-14
Supported by:
This work is partially supported by the National Key Research and Development Program of China (2017YFC0804401).

摘要/Abstract

摘要： 针对煤矿领域知识抽取中存在的术语嵌套、一词多义，抽取任务间存在误差传播等问题，提出了一种深层注意力模型框架。首先，使用标注策略联合学习两项知识抽取子任务，以解决误差传播的问题；其次，提出结合多种词向量信息的投影方法，以缓解煤矿领域术语抽取中的一词多义的问题；然后，设计深度特征提取网络，并提出深层注意力模型及两种模型增强方案来充分提取语义信息；最后，对模型的分类层进行研究，以在保证抽取效果的前提下最大限度地简化模型。实验结果表明，在煤矿领域语料上，相较于编码-解码结构的最好模型，所提模型的F1值有了1.5个百分点的提升，同时模型训练速度几乎提高至原来的3倍。该模型可有效地完成煤矿领域术语抽取以及术语关系抽取这两项知识抽取子任务。

关键词: 命名实体识别, 关系抽取, 联合学习, 注意力机制, 词向量

Abstract: In view of the problems of term nesting, polysemy and error propagation between extraction subtask tasks, a deep attention model framework was proposed. First, the annotation strategy was used to jointly learn two sub tasks of knowledge extraction for solving the problem of error propagation. Second, a projection method combining multiple word vector information was proposed to alleviate the polysemy problem in term extraction in coal mine field. Third, a deep feature extraction network was designed, and a deep attention model and two model enhancement schemes were proposed to fully extract the semantic information. Finally, the classification layer of the model was analyzed to simplify the model to the maximum extent under the premise of ensuring the extraction effect. Experimental results show that, compared with the best model of coding-decoding structure, the proposed model has the F1-score increased by 1.5 percentage points and the model training speed improved by nearly 3 times. The proposed model can effectively complete two knowledge extraction subtasks which are term extraction and term relationship extraction in coal mine field.

Key words: named entity recognition, relation extraction, joint learning, attention mechanism, word vector

中图分类号:

TP391

张心怡, 冯仕民, 丁恩杰. 面向煤矿的实体识别与关系抽取模型[J]. 计算机应用, 2020, 40(8): 2182-2188.

ZHANG Xinyi, FENG Shimin, DING Enjie. Entity recognition and relation extraction model for coal mine[J]. Journal of Computer Applications, 2020, 40(8): 2182-2188.

参考文献

[1] 张海楠,伍大勇,刘悦,等. 基于深度神经网络的中文命名实体识别[J]. 中文信息学报, 2017, 31(4):28-35. ZHANG H N, WU D Y, LIU Y, et al. Chinese Named Entity Recognition based on deep neural network[J]. Journal of Chinese Information Processing, 2017, 31(4):28-35.
[2] BIKEL D M, SCHWARTZ R, WEISCHEDEL R M. An algorithm that learns what's in a name[J]. Machine Learning, 1999, 34(1/2/3):211-231.
[3] 张玥杰,徐智婷,薛向阳. 融合多特征的最大熵汉语命名实体识别模型[J]. 计算机研究与发展, 2008, 45(6):1004-1010. (ZHANG Y J, XU Z T, XUE X Y. Fusion of multiple features for Chinese named entity recognition based on maximum entropy model[J]. Journal of Computer Research and Development, 2008, 45(6):1004-1010.)
[4] ARTALEJO J R, LOPEZ-HERRERO M J. The SIS and SIR stochastic epidemic models:a maximum entropy approach[J]. Theoretical Population Biology, 2011, 80(4):256-264.
[5] SONG S, ZHANG N, HUANG H. Named entity recognition based on conditional random fields[J]. Cluster Computing, 2019, 22(S3):5195-5206.
[6] LU J, YE M, TANG Z, et al. A novel method for Chinese named entity recognition based on character vector[C]//Proceedings of the 11th International Conference on Collaborative Computing:Networking, Applications and Worksharing, LNICST 163. Cham:Springer, 2015:141-150.
[7] DONG C, ZHANG J, ZONG C, et al. Character-based LSTMCRF with radical-level features for Chinese named entity recognition[C]//Proceedings of the 5th CCF Conference on Natural Language Processing and Chinese Computing, and 24th International Conference on Computer Processing of Oriental Languages, LNCS 10102. Cham:Springer, 2016:239-250.
[8] 王博冉,林夏,朱晓东,等. Lattice LSTM神经网络法中文医学文本命名实体识别模型研究[J]. 中国卫生信息管理杂志, 2019, 16(1):84-88. (WANG B R, LIN X, ZHU X D, et al. Chinese Name language Entity Recognition (NER) using lattice LSTM in medical language[J]. Chinese Journal of Health Information Management, 2019, 16(1):84-88.)
[9] 柏兵,侯霞,石松. 基于CRF和BI-LSTM的命名实体识别方法[J]. 北京信息科技大学学报, 2018, 33(6):27-33. (BAI B, HOU X, SHI S. Named entity recognition method based on CRF and BI-LSTM[J]. Journal of Beijing University of Information Technology, 2018, 33(6):27-33.)
[10] 李明扬,孔芳. 融入自注意力机制的社交媒体命名实体识别[J]. 清华大学学报(自然科学版), 2019, 59(6):461-467. (LI M Y, KONG F. Combined self-attention mechanism for named entity recognition in social media[J]. Journal of Tsinghua University (Natural Science Edition), 2019, 59(6):461-467.)
[11] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st Conference on Neural Information and Processing Systems. Red Hook, NY:Curran Associates Inc., 2017:6000-6010.
[12] ZHENG S, WANG F, BAO H, et al. Joint extraction of entities and relations based on a novel tagging scheme[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2017:1227-1236.
[13] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[EB/OL].[2019-11-12].https://arxiv.org/pdf/1301.3781.pdf.
[14] JOULIN A, GRAVE E, BOJANOWSKI P, et al. Bag of tricks for efficient text classification[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2017:427-431.
[15] PENNINGTON J, SOCHER R, MANNING C. Glove:global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:Association for Computational Linguistics, 2014:1532-1543.
[16] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:770-778.
[17] JOZEFOWICZ R, ZAREMBA W, SUTSKEVER I. An empirical exploration of recurrent network architectures[C]//Proceedings of the 32nd International Conference on International Conference on Machine Learning. New York:JMLR.org, 2015:2342-2350.
[18] GORMLEY M R, YU M, DREDZE M. Improved relation extraction with feature-rich compositional embedding models[C]//Proceedings of the 2015 Conference on Empirical Method in Natural Language Processing. Stroudsburg, PA:Association for Computational Linguistics, 2015:1774-1784.
[19] TANG J, QU M, WANG M, et al. LINE:large-scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web. Republic and Canton of Geneva:International World Wide Web Conferences Steering Committee, 2015:1067-1077.
[20] HOFFMANN R, ZHANG C, LING X, et al. Knowledge-based weak supervision for information extraction of overlapping relations[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg, PA:Association for Computational Linguistics, 2011:541-550.
[21] LI Q, JI H. Incremental joint extraction of entity mentions and relations[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2014:402-412.

面向煤矿的实体识别与关系抽取模型

Entity recognition and relation extraction model for coal mine

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