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

DOI:

https://doi.org/10.1609/aaai.v36i11.21696

Keywords:

Aspect Detection, Aspect Identification, Aspect Classification, Knowledge Graph

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.
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Published

2022-06-28

How to Cite

Zheng, Z., Cai, Y., & Li, L. (2022). Enhance Weakly-Supervised Aspect Detection with External Knowledge (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13119-13120. https://doi.org/10.1609/aaai.v36i11.21696

Issue

Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program