High-Quality Train Data Generation for Deep Learning-Based Web Page Classification Models
Jeong-Jae Kim, Byung-Won On, Ingyu Lee · IEEE Access · 2021
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 F1-score of the deep learning models (FNN, CNN, Bi-LSTM, and SeqGAN) trained with our proposed algorithm shows up to 0.9992 in F1-score.