Aspect-Level Sentiment Classification with Conv-Attention Mechanism | |
Yi Qian; Liu Jie; Zhang Guixuan; Zhang Shuwu | |
2018 | |
会议名称 | International Conference on Neural Information Processing (ICONIP) |
会议日期 | 2018.12.13-2018.12.16 |
会议地点 | Siem Reap, Cambodia |
出版者 | Springer |
摘要 | The aim of aspect-level sentiment classification is to identify the sentiment polarity of a sentence about a target aspect. Existing methods model the context sequence with recurrent network and employ attention mechanism to generate aspect-specific representations. In this paper, we introduce a novel mechanism called Conv-Attention, which can model the sequential information of context words and generate the aspect-specific attention at the same time via a convolution operation. Based on the new mechanism, we design a new framework for aspect-level sentiment classification called Conv-Attention Network (CAN). Compared to the previous attention-based recurrent models, the Conv-Attention Network can compute much faster. Extensive experimental results show that our model achieves the state-of-the-art performance while saving considerable time in model training and inferring. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/26110 |
专题 | 数字内容技术与服务研究中心_版权智能与文化计算 |
通讯作者 | Zhang Guixuan |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yi Qian,Liu Jie,Zhang Guixuan,et al. Aspect-Level Sentiment Classification with Conv-Attention Mechanism[C]:Springer,2018. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Aspect-Level Sentime(1027KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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