@inproceedings{bao-etal-2023-opinion,
title = "Opinion Tree Parsing for Aspect-based Sentiment Analysis",
author = "Bao, Xiaoyi and
Jiang, Xiaotong and
Wang, Zhongqing and
Zhang, Yue and
Zhou, Guodong",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.505",
doi = "10.18653/v1/2023.findings-acl.505",
pages = "7971--7984",
abstract = "Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. These models avoid explicit modeling of structure between sentiment elements, which are succinct yet lack desirable properties such as structure well-formedness guarantees or built-in elements alignments. In this study, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which can explicitly reveal a more comprehensive and complete aspect-level sentiment structure. In particular, we first introduce a novel context-free opinion grammar to normalize the sentiment structure. We then employ a neural chart-based opinion tree parser to fully explore the correlations among sentiment elements and parse them in the opinion tree form. Extensive experiments show the superiority of our proposed model and the capacity of the opinion tree parser with the proposed context-free opinion grammar. More importantly, our model is much faster than previous models.",
}
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<abstract>Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. These models avoid explicit modeling of structure between sentiment elements, which are succinct yet lack desirable properties such as structure well-formedness guarantees or built-in elements alignments. In this study, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which can explicitly reveal a more comprehensive and complete aspect-level sentiment structure. In particular, we first introduce a novel context-free opinion grammar to normalize the sentiment structure. We then employ a neural chart-based opinion tree parser to fully explore the correlations among sentiment elements and parse them in the opinion tree form. Extensive experiments show the superiority of our proposed model and the capacity of the opinion tree parser with the proposed context-free opinion grammar. More importantly, our model is much faster than previous models.</abstract>
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%0 Conference Proceedings
%T Opinion Tree Parsing for Aspect-based Sentiment Analysis
%A Bao, Xiaoyi
%A Jiang, Xiaotong
%A Wang, Zhongqing
%A Zhang, Yue
%A Zhou, Guodong
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F bao-etal-2023-opinion
%X Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. These models avoid explicit modeling of structure between sentiment elements, which are succinct yet lack desirable properties such as structure well-formedness guarantees or built-in elements alignments. In this study, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which can explicitly reveal a more comprehensive and complete aspect-level sentiment structure. In particular, we first introduce a novel context-free opinion grammar to normalize the sentiment structure. We then employ a neural chart-based opinion tree parser to fully explore the correlations among sentiment elements and parse them in the opinion tree form. Extensive experiments show the superiority of our proposed model and the capacity of the opinion tree parser with the proposed context-free opinion grammar. More importantly, our model is much faster than previous models.
%R 10.18653/v1/2023.findings-acl.505
%U https://aclanthology.org/2023.findings-acl.505
%U https://doi.org/10.18653/v1/2023.findings-acl.505
%P 7971-7984
Markdown (Informal)
[Opinion Tree Parsing for Aspect-based Sentiment Analysis](https://aclanthology.org/2023.findings-acl.505) (Bao et al., Findings 2023)
ACL
- Xiaoyi Bao, Xiaotong Jiang, Zhongqing Wang, Yue Zhang, and Guodong Zhou. 2023. Opinion Tree Parsing for Aspect-based Sentiment Analysis. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7971–7984, Toronto, Canada. Association for Computational Linguistics.