Yanyan Wang


2023

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Supervised Gradual Machine Learning for Aspect-Term Sentiment Analysis
Yanyan Wang | Qun Chen | Murtadha H.M. Ahmed | Zhaoqiang Chen | Jing Su | Wei Pan | Zhanhuai Li
Transactions of the Association for Computational Linguistics, Volume 11

Recent work has shown that Aspect-Term Sentiment Analysis (ATSA) can be effectively performed by Gradual Machine Learning (GML). However, the performance of the current unsupervised solution is limited by inaccurate and insufficient knowledge conveyance. In this paper, we propose a supervised GML approach for ATSA, which can effectively exploit labeled training data to improve knowledge conveyance. It leverages binary polarity relations between instances, which can be either similar or opposite, to enable supervised knowledge conveyance. Besides the explicit polarity relations indicated by discourse structures, it also separately supervises a polarity classification DNN and a binary Siamese network to extract implicit polarity relations. The proposed approach fulfills knowledge conveyance by modeling detected relations as binary features in a factor graph. Our extensive experiments on real benchmark data show that it achieves the state-of-the-art performance across all the test workloads. Our work demonstrates clearly that, in collaboration with DNN for feature extraction, GML outperforms pure DNN solutions.

2021

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DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis
Murtadha Ahmed | Qun Chen | Yanyan Wang | Youcef Nafa | Zhanhuai Li | Tianyi Duan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021