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
[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.
肖连杰,孟涛,王伟,吴志祥. 基于深度学习的情报分析方法识别研究 * ——以安全情报领域为例[J]. 数据分析与知识发现, 2019, 3(10): 20-28.
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.
( 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].
( 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.)
( Hua Bolin . Extracting Information Method Term from Chinese Academic Literature[J]. New Technology of Library and Information Service, 2013(6):68-75.)
( Deng Sanhong, Guo Hua . Intelligence Study and Intelligence Work Development Forum(2017)[J]. Documentation, Information and Knowledge, 2017(6):125-127.)
( 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.)
( 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.)
( Lu Wei, Ju Yuan, Zhang Xiaojuan . Research on Product Named Entity Feature Selection and Recognition[J]. Documentation, Information and Knowledge, 2012(3):4-12.)
( 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.)
( 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.
( 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.)
( 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.)
( 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.)
( Shen Si, Zhu Danhao . Chinese Place Name Recognition Based on Deep Learning[J]. Transactions of Beijing Institute of Technology, 2017,37(11):1150-1155.)
( 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.)
( 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.
( 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.
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.)
( China Encyclopedia General Committee . Encyclopedia of China: Library, Intelligence Study, Archives [M]. Beijing: Encyclopedia of China Publishing House, 1993.)
( 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.)