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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (1): 102-112    DOI: 10.11925/infotech.2096-3467.2022.0358
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Identifying Interdisciplinary Sci-Tech Literature Based on Multi-Label Classification
Wang Weijun1,2,Ning Zhiyuan1,2,Du Yi1,2(),Zhou Yuanchun1,2
1Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
2University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

[Objective] This paper tries to identify interdisciplinary sci-tech literature, aiming to find emerging interdisciplinary issues. [Methods] We combined the discipline labels of sci-tech literature provided by specialists with labels predicted by text classification algorithms to find interdisciplinary studies. [Results] The F1 value of the proposed method reached 0.45, which was 0.22 higher than those of the model-based predictions. [Limitations] The model had low recall values for identifying the interdisciplinary sci-tech research. [Conclusions] The paper effectively addresses the classification issues of interdisciplinary sci-tech literature, which merits more studies in the future.
Key words： Deep Learning      Multi-Label Text Classification      Interdisciplinary Research Recognition      Sci-Tech Literature     
Received: 18 April 2022      Published: 16 February 2023
ZTFLH:  TP393 G250  
Fund:Strategic Priority Research Program of Chinese Academy of Sciences(XDA16021400);National Natural Science Foundation of China(61836013);Youth Innovation Promotion Association,CAS(2021166)
Corresponding Authors: Du Yi，ORCID：0000-0003-3121-8937，E-mail：duyi@cnic.cn。   
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Weijun Wang
Zhiyuan Ning
Yi Du
Yuanchun Zhou

Cite this article:

Wang Weijun, Ning Zhiyuan, Du Yi, Zhou Yuanchun. Identifying Interdisciplinary Sci-Tech Literature Based on Multi-Label Classification. Data Analysis and Knowledge Discovery, 2023, 7(1): 102-112.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0358     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I1/102

Tree-Like Hierarchical of Disciplinary Classification System
Text Classification Flowchart
Identification Model for Interdisciplinary Research of Sci-Tech Literature
Encoder模型 Micro-F1 Macro-F1
FastText 0.781 8 0.535 1
TextCNN 0.778 2 0.524 6
TextRNN 0.714 7 0.300 4
TextRCNN 0.780 2 0.587 9
TextDPCNN 0.769 0 0.590 5
TextCRNN 0.757 3 0.485 8
HAN 0.738 9 0.487 5
BERT 0.791 8 0.597 6
Performance of Different Models
Distribution of the Projects Marked as Different Discipline Branches（2011-2018）
Distribution of the Projects Marked as Different Discipline Branches（2019）
参数 参数设置
Encoder模型 BERT
Transformer层数 4
batchsize 64
学习率 5e-5
epochs 100
早停参数 20
预训练模型 chinese_L-12_H-768_A-12
标签判断阈值 0.5
Classification Model Parameter Setting
数据集 Macro-F1 Micro-F1
关键词 0.564 4 0.788 3
关键词+题目 0.582 3 0.786 7
关键词+题目+摘要 0.597 6 0.791 8
Classification Performance on Different Data Sets
方法 分类类别 准确率 召回率 F1
S1 0（431条） 0.90 1 0.95
S1 1（ 55条） 1 0.13 0.23
S2 0（431条） 0.92 0.99 0.95
S2 1（ 55条） 0.85 0.31 0.45
The Effects of Interdisciplinary Research Identification Methods
序号 题目 关键词 实际学科 预测学科 人工标注
0 社交网络互动中用户“信息窄化”机理分析：基于微博的数据挖掘 网络信息内容； 信息沟； 社交媒体挖掘； 信息窄化； 微博舆情 G0414； G04； G F06； G0414； G04； F 交叉研究
1 面向攻击语境的社交网络危害信息检测方法及其验证与测试研究 验证与测试； 对抗攻击； 危害信息检测； 社交网络信息传播； 人工智能安全 F06； F0608； F F06； G01； F 交叉研究
2 基于多模态深度学习的金融跨市场耦合关系建模及应用研究 危机预警； 数量——文本双模态信息； 金融跨市场耦合关系； 深度学习 G01； G0114； G F06； G01； G0114； G； F 交叉研究
3 面向边计算服务质量优化的博弈论方法研究 nash均衡； pareto最优； 行为模型； 决策论； 移动边计算 F06； F； F0601 F06； G01； G0114； G 交叉研究
… … … … … …
Projects Identified as Interdisciplinary Research
序号 题目 关键词 实际学科 预测学科 人工标注
0 人工智能环境下层次化复杂问题决策方法研究 人工智能； 决策方法； 层次化复杂问题； 认知表达； 体系建模 F06； F； F0601 G01； G0114； G 学科交叉
1 云平台中多云用户联合博弈服务机制与策略探索 定价机制； 决策论； 机制设计； nash均衡 F06； F； F0601 G01； G0114； G 学科交叉
2 基于非确定性概率型信息的智能安全决策系统研究 智能决策系统； 不确定性知识表示； 安全博弈； 不确定性推理； 不确定性下的决策 F06； F； F0601 G01； G0114； G 学科交叉
3 基于标签语义挖掘的城市画像计算与应用模型研究 知识发现； 语义挖掘； 社会化标签； 城市画像； 信息融合 G0414； G04； G F0607； F06； F 学科交叉
… … … … … …
Projects Identified as Other Discipline
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