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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (10): 58-69    DOI: 10.11925/infotech.2096-3467.2020.0219
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Semi-Supervised Method for Text Classification Based on DW-TCI
Yu Bengong1,2,Ji Haomin1()
1School of Management, Hefei University of Technology, Hefei 230009, China
2Key Laboratory of Process Optimization & Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009, China
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[Objective] This paper proposes a new semi-supervised method for text classification, aiming to efficiently process texts with only small amount of annotations.[Methods] The proposed DW-TCI based method used double-channel feature extraction to obtain two sets of feature input vectors of the base classifier group. Then, we introduced the semi-supervised classification method with divergence and the idea of integrated learning. Finally, we trained the non-supervised sample with our model, and obtained the classification result of the predicted text with the equivalent weighted voting method.[Results] We examined our method with two different data sets having 20% labeled samples. The classification accuracy reached 92.32% and 87.01%, which were at least 5.54% and 5.65% higher than those of similar methods.[Limitations] The sample data set needs to be expanded.[Conclusions] The proposed method could reduce the labeling workloads of training samples and provide effective support for better text classification results.

Key wordsSemi-Supervised Classification      Sample Divergence      Classifier Divergence      Ensemble Learning     
Received: 19 March 2020      Published: 28 July 2020
ZTFLH:  TP391  
Corresponding Authors: Ji Haomin     E-mail:

Cite this article:

Yu Bengong,Ji Haomin. Semi-Supervised Method for Text Classification Based on DW-TCI. Data Analysis and Knowledge Discovery, 2020, 4(10): 58-69.

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DW-TCI Model Structure
CBOW and Skip-gram Structure
Structure of the Base Classifier Group
Classification Flowchart of DW-TCI Model
数据项 汽车评论 搜狗新闻
来源 汽车之家 搜狗实验室开源数据集
类别数(个) 2 5
数量(条) 8334/9195 2000/2000/2000/2000/2000
平均长度(字符) 45 843
最短长度(字符) 3 30
最长长度(字符) 1 519 19 870
Data Set
Classification Effect Evaluation
The Classification Accuracy of Different Encoding Methods
The Effects of Each Semi-Supervised Text Classification Model
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