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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 16: AAAI-21 Technical Tracks 16

An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis

February 1, 2023

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Authors

Yan Zhou

Institute of Information Engineering, Chinese Academy of Sciences


Fuqing Zhu

Institute of Information Engineering, Chinese Academy of Sciences


Pu Song

Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences


Jizhong Han

Institute of Information Engineering, Chinese Academy of Sciences


Tao Guo

Institute of Information Engineering, Chinese Academy of Sciences


Songlin Hu

Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences


DOI:

10.1609/aaai.v35i16.17719


Abstract:

Cross-domain aspect-based sentiment analysis aims to utilize the useful knowledge in a source domain to extract aspect terms and predict their sentiment polarities in a target domain. Recently, methods based on adversarial training have been applied to this task and achieved promising results. In such methods, both the source and target data are utilized to learn domain-invariant features through deceiving a domain discriminator. However, the task classifier is only trained on the source data, which causes the aspect and sentiment information lying in the target data can not be exploited by the task classifier. In this paper, we propose an Adaptive Hybrid Framework (AHF) for cross-domain aspect-based sentiment analysis. We integrate pseudo-label based semi-supervised learning and adversarial training in a unified network. Thus the target data can be used not only to align the features via the training of domain discriminator, but also to refine the task classifier. Furthermore, we design an adaptive mean teacher as the semi-supervised part of our network, which can mitigate the effects of noisy pseudo labels generated on the target data. We conduct experiments on four public datasets and the experimental results show that our framework significantly outperforms the state-of-the-art methods.

Topics: AAAI

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HOW TO CITE:

Yan Zhou||Fuqing Zhu||Pu Song||Jizhong Han||Tao Guo||Songlin Hu An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis Proceedings of the AAAI Conference on Artificial Intelligence (2021) 14630-14637.

Yan Zhou||Fuqing Zhu||Pu Song||Jizhong Han||Tao Guo||Songlin Hu An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis AAAI 2021, 14630-14637.

Yan Zhou||Fuqing Zhu||Pu Song||Jizhong Han||Tao Guo||Songlin Hu (2021). An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 14630-14637.

Yan Zhou||Fuqing Zhu||Pu Song||Jizhong Han||Tao Guo||Songlin Hu. An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.14630-14637.

Yan Zhou||Fuqing Zhu||Pu Song||Jizhong Han||Tao Guo||Songlin Hu. 2021. An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis. "Proceedings of the AAAI Conference on Artificial Intelligence". 14630-14637.

Yan Zhou||Fuqing Zhu||Pu Song||Jizhong Han||Tao Guo||Songlin Hu. (2021) "An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis", Proceedings of the AAAI Conference on Artificial Intelligence, p.14630-14637

Yan Zhou||Fuqing Zhu||Pu Song||Jizhong Han||Tao Guo||Songlin Hu, "An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis", AAAI, p.14630-14637, 2021.

Yan Zhou||Fuqing Zhu||Pu Song||Jizhong Han||Tao Guo||Songlin Hu. "An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.14630-14637.

Yan Zhou||Fuqing Zhu||Pu Song||Jizhong Han||Tao Guo||Songlin Hu. "An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 14630-14637.

Yan Zhou||Fuqing Zhu||Pu Song||Jizhong Han||Tao Guo||Songlin Hu. An Adaptive Hybrid Framework for Cross-domain Aspect-based Sentiment Analysis. AAAI[Internet]. 2021[cited 2023]; 14630-14637.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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