Convolutional Multi-Head Self-Attention on Memory for Aspect Sentiment Classification
文献类型：期刊论文
| 作者 | Yaojie Zhang; Bing Xu; Tiejun Zhao |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2020 |
| 卷号 | 7期号:4页码:1038-1044 |
| 关键词 | Aspect sentiment classification deep learning memory network sentiment analysis (SA) |
| ISSN号 | 2329-9266 |
| DOI | 10.1109/JAS.2020.1003243 |
| 英文摘要 | This paper presents a method for aspect based sentiment classification tasks, named convolutional multi-head self-attention memory network (CMA-MemNet). This is an improved model based on memory networks, and makes it possible to extract more rich and complex semantic information from sequences and aspects. In order to fix the memory network’s inability to capture context-related information on a word-level, we propose utilizing convolution to capture n-gram grammatical information. We use multi-head self-attention to make up for the problem where the memory network ignores the semantic information of the sequence itself. Meanwhile, unlike most recurrent neural network (RNN) long short term memory (LSTM), gated recurrent unit (GRU) models, we retain the parallelism of the network. We experiment on the open datasets SemEval-2014 Task 4 and SemEval-2016 Task 6. Compared with some popular baseline methods, our model performs excellently. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/43011]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Yaojie Zhang,Bing Xu,Tiejun Zhao. Convolutional Multi-Head Self-Attention on Memory for Aspect Sentiment Classification[J]. IEEE/CAA Journal of Automatica Sinica,2020,7(4):1038-1044. |
| APA | Yaojie Zhang,Bing Xu,&Tiejun Zhao.(2020).Convolutional Multi-Head Self-Attention on Memory for Aspect Sentiment Classification.IEEE/CAA Journal of Automatica Sinica,7(4),1038-1044. |
| MLA | Yaojie Zhang,et al."Convolutional Multi-Head Self-Attention on Memory for Aspect Sentiment Classification".IEEE/CAA Journal of Automatica Sinica 7.4(2020):1038-1044. |
入库方式： OAI收割
来源：自动化研究所
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