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TextCNN-based ensemble learning model for Japanese Text Multi-classification
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AI
DOI：10.1016/j.compeleceng.2023.108751.png)
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摘要
En 中文
In this paper, we aim at improving Japanese text classification using TextCNN-based ensemble learning model. Specifically, we first construct three different sub-classifiers, combining AL-BERT, RoBERTa, DistilBERT with TextCNN, respectively; and then explore the effectiveness of ensemble learning model to leverage complementary information from different sub-classifiers for better text classification. We also conduct a series of experiments with the dataset collected from Japanese Wikipedia pages, which was divided into 31 categories. The experimental results show that the proposed approach achieves a good performance. The accuracy, precision, recall and F1 scores reach 0.881, 0.884, 0.880 and 0.881, respectively, which shows that the TextCNN-based ensemble learning model can be used for Japanese Text Multi-Classification effectively.
Keyword:
ALBERT
RoBERTa
DistilBERT
TextCNN
Ensemble learning
Japanese text classification
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
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