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Enhance Weakly-Supervised Aspect Detection with External Knowledge (Student Abstract)

Authors

Zhuoming Zheng

School of Software and Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (SCUT), Ministry of Education, China


Yi Cai

School of Software and Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (SCUT), Ministry of Education, China


Liuwu Li

School of Software and Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (SCUT), Ministry of Education, China


Proceedings:

No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Student Abstract and Poster Program

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Abstract:

Aspect detection aims to identify aspects of reviews and is an essential up-stream task of opinion mining and so on. However, existing weakly-supervised methods suffer from lacking the ability of identifying implicit aspects with infrequent aspect terms and "Misc" aspects. To tackle these problems, we propose to enhance the representation of segment with external knowledge by a weakly-supervised method. Experiments demonstrate the effectiveness of our model and the improvement by incorporating external knowledge.

DOI:

10.1609/aaai.v36i11.21696


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36