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High-Quality Train Data Generation for Deep Learning-Based Web Page Classification Models
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DOI：10.1109/ACCESS.2021.3086586.png)
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摘要
En 中文
The current deep learning models detecting relevant web pages show low accuracy because of the poor quality of the training data. In this paper, we propose a novel algorithm to automatically generate high-quality training data based on the frequency of the document including the entity of interest. Our experimental results with movies and cellphones data sets show that the average F-1-score of the deep learning models (FNN, CNN, Bi-LSTM, and SeqGAN) trained with our proposed algorithm shows up to 0:9992 in F-1-score.
Keyword:
Text classification
deep learning
automatic labelling
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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