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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (10): 20-28    DOI: 10.11925/infotech.2096-3467.2018.1199
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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
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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:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1199     OR     http://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
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