Long Short-term Memory Network over Rhetorical Structure Theory for Sentence-level Sentiment Analysis


Xianghua Fu, Wangwang Liu, Yingying Xu, Chong Yu, Ting Wang ;
Proceedings of The 8th Asian Conference on Machine Learning, PMLR 63:17-32, 2016.


Using deep learning models to solve sentiment analysis of sentences is still a challenging task. Long short-term memory (LSTM) network solves the gradient disappeared problem existed in recurrent neural network (RNN), but LSTM structure is linear chain-structure that can’t capture text structure information. Afterwards, Tree-LSTM is proposed, which uses LSTM forget gate to skip sub-trees that have little effect on the results to get good performance. It illustrates that the chain-structured LSTM more strongly depends on text structure. However, Tree-LSTM can’t clearly figure out which sub-trees are important and which sub-trees have little effect. We propose a simple model which uses Rhetorical Structure Theory (RST) for text parsing. By building LSTM network on RST parse structure, we make full use of LSTM structural characteristics to automatically enhance the nucleus information and filter the satellite information of text. Furthermore, this approach can make the representations concerning the relations between segments of text, which can improve text semantic representations. Experiment results show that this method not only has higher classification accuracy, but also trains quickly.

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