| Li Ying (李颖),Shao Qing,Hao Weichen.[J].高技术通讯(英文),2021,27(3):320~328 |
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| Study on the fusion emotion classification of multiple characteristics based on attention mechanism |
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| DOI： |
| 中文关键词: |
| 英文关键词: multi-head attention (MHA), structured-self attention (SSA), emotion classification, deep learning, bidirectional long-short-term memory (BiLSTM) |
| 基金项目: |
| Author Name | Affiliation | | Li Ying (李颖) | (School of Optoelectronic Information and Intelligent Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China) | | Shao Qing | (School of Optoelectronic Information and Intelligent Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China) | | Hao Weichen | (School of Optoelectronic Information and Intelligent Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| The current research on emotional classification uses many methods that combine the attention mechanism with neural networks. However, the effect is unsatisfactory when dealing with complex text. An emotional classification model is proposed, which combines multi-head attention (MHA) with improved structured-self attention (SSA). The model makes several different linear transformations of input by introducing MHA mechanism and can extract more comprehensive high-level phrase representation features from the word embedded vector. Meanwhile, it can realize the parallelization calculation and ensure the training speed of the model. The improved SSA structure uses matrices to represent different parts of a sentence to extract local key information, to ensure that the degree of dependence between words is not affected by time and sentence length, and generate the overall semantics of the sentence. Experiment results show that the current model effectively obtains global structural information and improves classification accuracy. |
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