Combing Transfer Learning and Stacking Approach for Extreme Contents Detection

• Main Authors: Yi-Min Lu, 盧胤旻
• Other Authors: Chao-Chang Chiu
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/j82m33

碩士 === 元智大學 === 資訊管理學系 === 107 === In recent years, deep learning technology has been highly developed in image recognition, and is also widely used in natural language recognition and word exploration. This study uses migration learning techniques to analyze online reviews and then extract and amplify keywords through feature engineering. This study uses deep learning techniques to load the migration learning mechanism and text classification study. This study will add an Attention Layer into general deep neural network, then through combining multiple deep neural networks and Stacking technology, a final model is developed as for comments detection. The experimental results of detect the extreme comments show that using the deep neural network with Attention Layer, the detection results can be 66.19% in F1 measure and Auc: 96.05%. The combined deep neural network with Stacking technology approach can obtain F1 measure 69.96% and Auc: 96.17%. This study involved Kaggle nature language competition of extreme contents detection on Quora. The results of this study ranked within the top 16% of the global competition, F1 measure 70.13%, and the best winning result of 71.32%, with only 1.2% difference.