Zhongchen Miao


Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification
Hao Niu | Yun Xiong | Jian Gao | Zhongchen Miao | Xiaosu Wang | Hongrun Ren | Yao Zhang | Yangyong Zhu
Proceedings of the 29th International Conference on Computational Linguistics

Aspect-based sentiment analysis (ABSA) has drawn more and more attention because of its extensive applications. However, towards the sentence carried with more than one aspect, most existing works generate an aspect-specific sentence representation for each aspect term to predict sentiment polarity, which neglects the sentiment relationship among aspect terms. Besides, most current ABSA methods focus on sentences containing only one aspect term or multiple aspect terms with the same sentiment polarity, which makes ABSA degenerate into sentence-level sentiment analysis. In this paper, to deal with this problem, we construct a heterogeneous graph to model inter-aspect relationships and aspect-context relationships simultaneously and propose a novel Composition-based Heterogeneous Graph Multi-channel Attention Network (CHGMAN) to encode the constructed heterogeneous graph. Meanwhile, we conduct extensive experiments on three datasets: MAMSATSA, Rest14, and Laptop14, experimental results show the effectiveness of our method.


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Financial News Annotation by Weakly-Supervised Hierarchical Multi-label Learning
Hang Jiang | Zhongchen Miao | Yuefeng Lin | Chenyu Wang | Mengjun Ni | Jian Gao | Jidong Lu | Guangwei Shi
Proceedings of the Second Workshop on Financial Technology and Natural Language Processing