Title
Interactive Attention Networks for Aspect-Level Sentiment Classification.
Abstract
Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.
Year
DOI
Venue
2017
10.24963/ijcai.2017/568
IJCAI
DocType
Volume
Citations 
Journal
abs/1709.00893
43
PageRank 
References 
Authors
1.15
15
4
Name
Order
Citations
PageRank
Dehong Ma1614.73
Sujian Li268359.24
Xiaodong Zhang3884.51
Hou-Feng Wang461153.83