Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (10): 20-28    DOI: 10.11925/infotech.2096-3467.2018.1199
Current Issue | Archive | Adv Search |
Entity Recognition of Intelligence Method Based on Deep Learning: Taking Area of Security Intelligence for Example
Lianjie Xiao1,2(),Tao Meng1,2,Wei Wang1,2,Zhixiang Wu3
1School of Information Management, Nanjing University, Nanjing 210023, China
2Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
3School of Economics and Management, Nanjing University of Technology, Nanjing 211800, China
Download: PDF (1325 KB)   HTML ( 58
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper provides directions for a new scholarly system, aiming to identify and summarize intelligence analysis methods for security intelligence. [Methods] Firstly, we retrieved full-text security intelligence literature, and tagged them using Character-level method. Then, we constructed the corpus for the extraction of intelligence analysis methods. Finally, we compared the performance of two deep learning models with the experimental data. [Results] For the BiLSTM model, the precision, recall and F1 values were 81.71%, 77.26%, and 79.36% respectively. For the BiLSTM-CRF model, the precision, recall and F1 values were 84.71%, 79.25%, and 81.83%. [Limitations] The pronouns that represent intelligence analysis methods are not taken into consideration. [Conclusions] We could use deep learning model to extract intelligence analysis methods for security intelligence.

Key wordsSecurity Intelligence      Intelligence Analysis Methods      Entity Recognition      Bi-LSTM     
Received: 29 October 2018      Published: 25 November 2019
ZTFLH:  TP393 G35  
Corresponding Authors: Lianjie Xiao     E-mail: 1061939301@qq.com

Cite this article:

Lianjie Xiao,Tao Meng,Wei Wang,Zhixiang Wu. Entity Recognition of Intelligence Method Based on Deep Learning: Taking Area of Security Intelligence for Example. Data Analysis and Knowledge Discovery, 2019, 3(10): 20-28.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1199     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I10/20

编号 例句
1 采用内容分析法, 从研究热点和研究特点两方面对大数据环境下的竞争情报研究现状进行总结和分析。
2 为了解研究方法应用与研究领域的耦合情况, 构建基于研究方法应用的研究领域关键词的共现矩阵, 同时构建研究方法应用与研究领域的耦合矩阵, 并利用SPSS 19.0进行聚类分析和对应分析;
3 结构方程模型是一种建立、估计和检验因果关系模型的方法。
参数名 数值 参数名 数值
embedding_size 100 learning_rate 0.001
神经元数量 128 batch_size 60
隐藏层 32 epoch 200
layer_dropout 0.4 activation tanh
深度模型 评价指标
准确率P 召回率R F1
BiLSTM 80.81 80.61 80.71
80.17 69.28 74.33
83.83 83.12 83.47
80.89 79.75 80.31
81.94 76.89 79.34
均值 81.71 77.26 79.36
BiLSTM-CRF 85.97 78.69 82.17
83.66 83.43 83.55
85.39 83.83 84.60
80.62 75.48 77.97
87.92 74.82 80.84
均值 84.71 79.25 81.83
方法名称 提及
频次
方法名称 提及
频次
社会调查法(实地调查、专家咨询法、抽样调查、问卷调查) 788 时间序列分析法 164
分类(主题分类、文本分类) 623 深度神经网络 153
综合评价法 338 定标比超法 149
归纳法 331 竞争性假设分析 148
聚类分析法 305 结构化分析法 126
仿真 301 演绎法 125
数学方法(数学模型、统计分析法) 298 社会网络分析法 122
数据挖掘 284 回归分析法 101
比较分析法 284 头脑风暴法 84
案例分析法 282 系统分析法 83
相关性分析法 282 综述 71
内容分析法 281 情景分析法 58
可视化方法 257 逻辑方法 57
文献研究法(文献调研、计量研究) 216 共词分析 54
推理(类比推理、知识推理) 215 主成分分析法 47
检索(情报检索、信息检索) 182 哲学方法 35
引文分析法(专利引文分析、共被引分析) 167 价值链分析 22
[1] 高伟, 薛梦瑶, 于成成. 面向大数据的情报分析方法和技术体系研究[J/OL]. 情报理论与实践. [ 2019- 10- 14]. .
[1] ( Gao Wei, Xue Mengyao, Yu Chengcheng. Big Data-Oriented System of Intelligence Analysis Methods and Technologies [J/OL]. Information Studies: Theory & Application. [ 2019- 10- 14].
[2] 肖连杰, 成洁, 蒋勋. 大数据环境下国内情报分析研究方法研究[J/OL]. 情报理论与实践. [ 2019- 10- 14]. .
[2] ( Xiao Lianjie, Cheng Jie, Jiang Xun. Research on Intelligence Analysis Methods in the Era of Big Data in China[J/OL]. 情报理论与实践. [ 2019- 10- 14]. .
[3] 王强, 陈安琪 . 情报方法库研究[J]. 情报工程, 2015,1(1):95-102.
[3] ( Wang Qiang, Chen Anqi . The Study on the Intelligence Method Base[J]. Technology Intelligence Engineering, 2015,1(1):95-102.)
[4] 朱丹浩, 杨蕾, 王东波 . 基于深度学习的中文机构名识别研究——一种汉字级别的循环神经网络方法[J]. 数据分析与知识发现, 2017,1(12):36-43.
[4] ( Zhu Danhao, Yang Lei, Wang Dongbo . Recognizing Chinese Organization Names Based on Deep Learning: A Recurrent Network Model[J]. Data Analysis and Knowledge Discovery, 2017,1(12):36-43.)
[5] 化柏林 . 针对中文学术文献的情报方法术语抽取[J]. 现代图书情报技术, 2013(6):68-75.
[5] ( Hua Bolin . Extracting Information Method Term from Chinese Academic Literature[J]. New Technology of Library and Information Service, 2013(6):68-75.)
[6] 邓三鸿, 郭骅 . 情报学与情报工作发展论坛(2017) 隆重召开并凝聚形成《南京共识》[J]. 图书情报知识, 2017(6):125-127.
[6] ( Deng Sanhong, Guo Hua . Intelligence Study and Intelligence Work Development Forum(2017)[J]. Documentation, Information and Knowledge, 2017(6):125-127.)
[7] 谷俊, 王昊 . 基于领域中文文本的术语抽取方法研究[J]. 现代图书情报技术, 2011(4):29-34.
[7] ( Gu Jun, Wang Hao . Study on Term Extraction on the Basis of Chinese Domain Texts[J]. New Technology of Library and Information Service, 2011(4):29-34.)
[8] 牟冬梅, 金姗, 琚沅红 . 基于文献数据的疾病与基因关联关系研究[J]. 数据分析与知识发现, 2018,2(8):98-106.
[8] ( Mu Dongmei, Jin Shan, Ju Yuanhong . Finding Association Between Diseases and Genes from Literature Abstracts[J]. Data Analysis and Knowledge Discovery, 2018,2(8):98-106.)
[9] 陆伟, 鞠源, 张晓娟 , 等. 产品命名实体特征选择与识别研究[J]. 图书情报知识, 2012(3):4-12.
[9] ( Lu Wei, Ju Yuan, Zhang Xiaojuan . Research on Product Named Entity Feature Selection and Recognition[J]. Documentation, Information and Knowledge, 2012(3):4-12.)
[10] 何宇, 吕学强, 徐丽萍 . 新能源汽车领域中文术语抽取方法[J]. 现代图书情报技术, 2015(10):88-94.
[10] ( He Yu, Lv Xueqiang, Xu Liping . A Chinese Term Extraction System in New Energy Vehicles Domain[J]. New Technology of Library and Information Service, 2015(10):88-94.)
[11] 陈锋, 翟羽佳, 王芳 . 基于条件随机场的学术期刊中理论的自动识别方法[J]. 图书情报工作, 2016,60(2):122-128.
doi: 10.13266/j.issn.0252-3116.2016.02.019
[11] ( Chen Feng, Zhai Yujia, Wang Fang . Automatic Theory Recognition in Academic Journals Based on CRF[J]. Library and Information Service, 2016,60(2):122-128.)
doi: 10.13266/j.issn.0252-3116.2016.02.019
[12] Ju Z, Wang J, Zhu F . Named Entity Recognition from Biomedical Text Using SVM [C]//Proceedings of the 5th International Conference on Bioinformatics and Biomedical Engineering, Wuhan, China. IEEE, 2011: 1-4.
[13] Zhu F, Shen B . Combined SVM-CRFs for Biological Named Entity Recognition with Maximal Bidirectional Squeezing[J]. PLoS One, 2012,7(6):1-9.
[14] 王东波, 胡昊天, 周鑫 , 等. 基于深度学习的数据科学招聘实体自动抽取及分析研究[J]. 图书情报工作, 2018,62(13):64-73.
[14] ( Wang Dongbo, Hu Haotian, Zhou Xin , et al. Research of Automatic Extraction of Entities of Data Science Recruitment and Analysis Based on Deep Learning[J]. Library and Information Service, 2018,62(13):64-73.)
[15] 张帆, 王敏 . 基于深度学习的医疗命名实体识别[J]. 计算技术与自动化, 2017,36(1):123-127.
[15] ( Zhang Fan, Wang Min . Medical Text Entities Recognition Method Base on Deep Learning[J]. Computing Technology and Automation, 2017,36(1):123-127.)
[16] 孙娟娟, 于红, 冯艳红 , 等. 基于深度学习的渔业领域命名实体识别[J]. 大连海洋大学学报, 2018,33(2):265-269.
[16] ( Sun Juanjuan, Yu Hong, Feng Yanhong , et al. Recognition of Nominated Fishery Domain Entity Based on Deep Learning Architectures[J]. Journal of Dalian Ocean University, 2018,33(2):265-269.)
[17] 杨培, 杨志豪, 罗凌 , 等. 基于注意机制的化学药物命名实体识别[J]. 计算机研究与发展, 2018,55(7):1548-1556.
[17] ( Yang Pei, Yang Zhihao, Luo Ling , et al. An Attention-Based Approach for Chemical Compound and Drug Named Entity Recognition[J]. Journal of Computer Research and Development, 2018,55(7):1548-1556.)
[18] 沈思, 朱丹浩 . 基于深度学习的中文地名识别研究[J]. 北京理工大学学报, 2017,37(11):1150-1155.
[18] ( Shen Si, Zhu Danhao . Chinese Place Name Recognition Based on Deep Learning[J]. Transactions of Beijing Institute of Technology, 2017,37(11):1150-1155.)
[19] 朱丹浩, 杨蕾, 王东波 . 基于深度学习的中文机构名识别研究——一种汉字级别的循环神经网络方法[J]. 现代图书情报技术, 2016(12):36-43.
[19] ( Zhu Danhao, Yang Lei, Wang Dongbo . Recognizing Chinese Organization Names Based on Deep Learning: A Recurrent Network Model[J]. New Technology of Library and Information Service, 2016(12):36-43.)
[20] 隋臣 . 基于深度学习的中文命名实体识别研究[D]. 杭州: 浙江大学, 2017.
[20] ( Sui Chen . Research of Chinese Named Entity Recognition Based on Deep Learning[D]. Hangzhou: Zhejiang University, 2017.)
[21] 刘玉娇, 琚生根, 李若晨 , 等. 基于深度学习的中文微博命名实体识别[J]. 四川大学学报: 工程科学版, 2016,48(S2):142-146.
[21] ( Liu Yujiao, Ju Shenggen, Li Ruochen , et al. Chinese Microblog Named Entity Recognition in Chinese Micro-blog Based on Deep Learning[J]. Journal of Sichuan University: Engineering Science Edition, 2016,48(S2):142-146.)
[22] 何红磊 . 基于词表示方法的生物医学命名实体识别[D]. 大连: 大连理工大学, 2015.
[22] ( He Honglei . Research of Word Representations on Biomedical Named Entity Recognition[D]. Dalian: Dalian University of Technology, 2015.)
[23] Demir H, Ozgur A . Improving Named Entity Recognition for Morphologically Rich Languages Using Word Embeddings [C]// Proceedings of the 13th International Conference on Machine Learning & Applications, Detroit, MI, USA. IEEE, 2014: 117-122.
[24] 李丽双, 郭元凯 . 基于CNN-BLSTM-CRF模型的生物医学命名实体识别[J]. 中文信息学报, 2018,32(1):116-122.
[24] ( Li Lishuang, Guo Yuankai . Biomedical Named Entity Recognition with CNN-BLSTM-CRF[J]. Journal of Chinese Information Processing, 2018,32(1):116-122.)
[25] Pham T H, Le-Hong P. End-to-End Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-Level Vs. Character-Level [C]// Proceedings of the 15th International Conference of the Pacific Association for Computational Linguistics. Springer, 2017: 219-232.
[26] Hochreiter S, Schmidhuber J . Long Short-term Memory[J]. Neural Computation, 1997,9(8):1735-1780.
[27] Sutskever I, Vinyals O, Le Q V . Sequence to Sequence Learning with Neural Networks[A]//Advances in Neural Information Processing Systems[M]. Morgan Kaufmann Publishers, 2014: 3104-3112.
[28] Graves A, Mohamed A, Hinton G . Speech Recognition with Deep Recurrent Neural Networks [C]// Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2013: 6645-6649.
[29] Graves A, Schmidhuber J . Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures[J]. Neural Networks, 2005,18(5-6):602-610.
[30] 周志华 . 机器学习[M]. 北京: 清华大学出版社, 2016.
[30] ( Zhou Zhihua. Machine Learning[M]. Beijing: Tsinghua University Press, 2016.)
[31] 中国大百科全书总委员会《图书馆学情报学档案学》委员会. 中国大百科全书: 图书馆学情报学档案学[M]. 北京: 中国大百科全书出版社. 1993.
[31] ( China Encyclopedia General Committee . Encyclopedia of China: Library, Intelligence Study, Archives [M]. Beijing: Encyclopedia of China Publishing House, 1993.)
[32] 陈传夫, 马浩琴 . 图书情报学现实研究中科学方法应用的调查分析——以2010年的期刊论文为样本[J]. 图书馆论坛, 2011,31(6):32-37.
[32] ( Chen Chuanfu, Ma Haoqin . Survey Research on Implementation of Research Methods in Library and Information Science——Take the Journal Articles of 2010 as Sample[J]. Library Tribune, 2011,31(6):32-37.)
[1] Hu Haotian,Ji Jinfeng,Wang Dongbo,Deng Sanhong. An Integrated Platform for Food Safety Incident Entities Based on Deep Learning[J]. 数据分析与知识发现, 2021, 5(3): 12-24.
[2] Xu Chenfei, Ye Haiying, Bao Ping. Automatic Recognition of Produce Entities from Local Chronicles with Deep Learning[J]. 数据分析与知识发现, 2020, 4(8): 86-97.
[3] Gao Yuan,Shi Yuanlei,Zhang Lei,Cao Tianyi,Feng Jun. Reconstructing Tour Routes Based on Travel Notes[J]. 数据分析与知识发现, 2020, 4(2/3): 165-172.
[4] Ma Jianxia,Yuan Hui,Jiang Xiang. Extracting Name Entities from Ecological Restoration Literature with Bi-LSTM+CRF[J]. 数据分析与知识发现, 2020, 4(2/3): 78-88.
[5] Liu Liu,Qin Tianyun,Wang Dongbo. Automatic Extraction of Traditional Music Terms of Intangible Cultural Heritage[J]. 数据分析与知识发现, 2020, 4(12): 68-75.
[6] Liu Jingru,Song Yang,Jia Rui,Zhang Yipeng,Luo Yong,Ma Jingdong. A BiLSTM-CRF Model for Protected Health Information in Chinese[J]. 数据分析与知识发现, 2020, 4(10): 124-133.
[7] Han Huang,Hongyu Wang,Xiaoguang Wang. Automatic Recognizing Legal Terminologies with Active Learning and Conditional Random Field Model[J]. 数据分析与知识发现, 2019, 3(6): 66-74.
[8] Meishan Chen,Chenxi Xia. Identifying Entities of Online Questions from Cancer Patients Based on Transfer Learning[J]. 数据分析与知识发现, 2019, 3(12): 61-69.
[9] Qiang Lu,Zhenfang Zhu,Fuyong Xu,Qiangqiang Guo. Chinese Sentiment Classification Method with Bi-LSTM and Grammar Rules[J]. 数据分析与知识发现, 2019, 3(11): 99-107.
[10] Li Yu,Li Qian,Changlei Fu,Huaming Zhao. Extracting Fine-grained Knowledge Units from Texts with Deep Learning[J]. 数据分析与知识发现, 2019, 3(1): 38-45.
[11] Mu Dongmei,Jin Shan,Ju Yuanhong. Finding Association Between Diseases and Genes from Literature Abstracts[J]. 数据分析与知识发现, 2018, 2(8): 98-106.
[12] Tang Huihui,Wang Hao,Zhang Zixuan,Wang Xueying. Extracting Names of Historical Events Based on Chinese Character Tags[J]. 数据分析与知识发现, 2018, 2(7): 89-100.
[13] Feng Guoming,Zhang Xiaodong,Liu Suhui. DBLC Model for Word Segmentation Based on Autonomous Learning[J]. 数据分析与知识发现, 2018, 2(5): 40-47.
[14] Fan Xinyue,Cui Lei. Using Text Mining to Discover Drug Side Effects: Case Study of PubMed[J]. 数据分析与知识发现, 2018, 2(3): 79-86.
[15] Sui Mingshuang,Cui Lei. Extracting Chemical and Disease Named Entities with Multiple-Feature CRF Model[J]. 现代图书情报技术, 2016, 32(10): 91-97.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn