Abstract
Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets. The source codes are available at https://github.com/HSLCY/ABSA-BERT-pair.- Anthology ID:
- N19-1035
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 380–385
- Language:
- URL:
- https://aclanthology.org/N19-1035
- DOI:
- 10.18653/v1/N19-1035
- Cite (ACL):
- Chi Sun, Luyao Huang, and Xipeng Qiu. 2019. Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 380–385, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (Sun et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/N19-1035.pdf
- Code
- HSLCY/ABSA-BERT-pair + additional community code