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Sentiment Classification for Chinese Text Based on Interactive Multitask Learning
2020
IEEE Access
In this paper, an interactive multitask learning method for Chinese text sentiment classification is proposed. Here, the classic BiLSTM + attention + CRF model is used to obtain full use of the interaction relationship between tasks, and it simultaneously solves the two tasks of emotional dictionary expansion and sentiment classification. The proposed method divides text sentiment classification and emotional dictionary expansion into primary task and subtask, and it adopts the Enhanced
doi:10.1109/access.2020.3007889
fatcat:syhi27a6qrg2dc6z4asnpnlcky