Affiliations: [a] School of Software, Xin Jiang University, Urumqi, China
| [b] School of Information Science and Engineering, Xinjiang University, Urumqi, China
| [c] Xinjiang Laboratory of Multi-Language InformationTechnology, Xinjiang University, Urumqi, China
Correspondence:
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Corresponding author. Wenzhong Yang, School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China. E-mail: [email protected].
Abstract: Aspect-based sentiment analysis (ABSA) contains three subtasks, namely aspect term extraction, opinion term extraction and aspect-level sentiment classification. In order to make full use of the relationship between the three subtasks, some recent studies have successfully tried to use a unified framework to solve the problem of aspect-based sentiment analysis. However, these studies have not yet integrated domain knowledge into the model. Inspired by the post-training task, we propose a joint model (RACL-BERT-PT). This model combines the pre-training model BERT-PT with domain knowledge and the unified joint training framework RACL. The experimental results show that our model has achieved better results than previous experiments on three public data.
