.png){kind=icon}
返回
Is position important? deep multi-task learning for aspect-based sentiment analysis
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-020-01760-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The position information of aspect is essential and useful for aspect-based sentiment analysis, while how to model the position of the aspect effectively during aspect-based sentiment analysis has not been well studied. Inspired by the intuition that the position prediction can help boost the performance of aspect-based sentiment analysis, we propose a D eep M ulti-T ask L earning (DMTL) model, which handles sentiment prediction (SP) and position prediction (PP) simultaneously. In particular, we first use a shared layer to learn the common features of the two tasks. Then, two task-specific layers are utilized to learn the features specific to the tasks and perform position prediction and sentiment prediction in parallel. Inspired by autoencoder structure, we design a position-aware attention and a deep bi-directional LSTM (DBi-LSTM) model for sentiment prediction and position prediction respectively to capture the position information better. Extensive experiments on four benchmark datasets show that our approach can effectively improve the performance of aspect-based sentiment analysis compared with the strong baselines.
Keyword:
Aspect-based sentiment analysis
Multi-task
Deep learning
Position
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容