A Novel Teacher-Student Network for Sentiment Classification
PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRIAL ENGINEERING (AIIE 2016)(2016)
摘要
Compared with traditional text classification, many sentiments online such as product reviews are not standard, which are concise with clear standpoints. Researchers on sentiment classification face tremendous challenges. Although various sentiment analysis systems are available, they have many operation restrictions and are still far from perfect. In this paper, we propose a novel approach, Teacher-Student Network (TSN), for automatically classifying the sentiment of reviews. Teacher-Student Network Model is composed of one teacher network and one student network. Teacher network is a Naive Bayes model. Student network is deep neural networks model. Our approach can transfer knowledge between different models and requires less training data. Experimental results on different domain datasets show that when we employ full training data, our model can achieve similar performance to RNN(Recurrent Neural Network) model and when we reduce training data, our model achieve better performance than RNN.
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关键词
sentiment classification,knowledge transfer,deep learning,RNN
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