Abstract
Aspect-level sentiment analysis (ASA) has received much attention in recent years. Most existing approaches tried to leverage syntactic information, such as the dependency parsing results of the input text, to improve sentiment analysis on different aspects. Although these approaches achieved satisfying results, their main focus is to leverage the dependency arcs among words where the dependency type information is omitted; and they model different dependencies equally where the noisy dependency results may hurt model performance. In this paper, we propose an approach to enhance aspect-level sentiment analysis with word dependencies, where the type information is modeled by key-value memory networks and different dependency results are selectively leveraged. Experimental results on five benchmark datasets demonstrate the effectiveness of our approach, where it outperforms baseline models on all datasets and achieves state-of-the-art performance on three of them.- Anthology ID:
- 2021.eacl-main.326
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3726–3739
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.326
- DOI:
- 10.18653/v1/2021.eacl-main.326
- Cite (ACL):
- Yuanhe Tian, Guimin Chen, and Yan Song. 2021. Enhancing Aspect-level Sentiment Analysis with Word Dependencies. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3726–3739, Online. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Aspect-level Sentiment Analysis with Word Dependencies (Tian et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.eacl-main.326.pdf
- Code
- cuhksz-nlp/asa-wd