@inproceedings{chai-etal-2023-aspect,
title = "Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis",
author = "Chai, Heyan and
Yao, Ziyi and
Tang, Siyu and
Wang, Ye and
Nie, Liqiang and
Fang, Binxing and
Liao, Qing",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.727",
doi = "10.18653/v1/2023.findings-emnlp.727",
pages = "10902--10913",
abstract = "Aspect-based sentiment analysis (ABSA) aims to align aspects and corresponding sentiment expressions, so as to identify the sentiment polarities of specific aspects. Most existing ABSA methods focus on mining syntactic or semantic information, which still suffers from noisy interference introduced by the attention mechanism and dependency tree when multiple aspects exist in a sentence. To address these issues, in this paper, we revisit ABSA from a novel perspective by proposing a novel scope-assisted multi-view graph contrastive learning framework. It not only mitigates noisy interference for better locating aspect and its corresponding sentiment opinion with aspect-specific scope, but also captures the correlation and difference between sentiment polarities and syntactic/semantic information. Extensive experiments on five benchmark datasets show that our proposed approach substantially outperforms state-of-the-art methods and verifies the effectiveness and robustness of our model.",
}
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<abstract>Aspect-based sentiment analysis (ABSA) aims to align aspects and corresponding sentiment expressions, so as to identify the sentiment polarities of specific aspects. Most existing ABSA methods focus on mining syntactic or semantic information, which still suffers from noisy interference introduced by the attention mechanism and dependency tree when multiple aspects exist in a sentence. To address these issues, in this paper, we revisit ABSA from a novel perspective by proposing a novel scope-assisted multi-view graph contrastive learning framework. It not only mitigates noisy interference for better locating aspect and its corresponding sentiment opinion with aspect-specific scope, but also captures the correlation and difference between sentiment polarities and syntactic/semantic information. Extensive experiments on five benchmark datasets show that our proposed approach substantially outperforms state-of-the-art methods and verifies the effectiveness and robustness of our model.</abstract>
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%0 Conference Proceedings
%T Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis
%A Chai, Heyan
%A Yao, Ziyi
%A Tang, Siyu
%A Wang, Ye
%A Nie, Liqiang
%A Fang, Binxing
%A Liao, Qing
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chai-etal-2023-aspect
%X Aspect-based sentiment analysis (ABSA) aims to align aspects and corresponding sentiment expressions, so as to identify the sentiment polarities of specific aspects. Most existing ABSA methods focus on mining syntactic or semantic information, which still suffers from noisy interference introduced by the attention mechanism and dependency tree when multiple aspects exist in a sentence. To address these issues, in this paper, we revisit ABSA from a novel perspective by proposing a novel scope-assisted multi-view graph contrastive learning framework. It not only mitigates noisy interference for better locating aspect and its corresponding sentiment opinion with aspect-specific scope, but also captures the correlation and difference between sentiment polarities and syntactic/semantic information. Extensive experiments on five benchmark datasets show that our proposed approach substantially outperforms state-of-the-art methods and verifies the effectiveness and robustness of our model.
%R 10.18653/v1/2023.findings-emnlp.727
%U https://aclanthology.org/2023.findings-emnlp.727
%U https://doi.org/10.18653/v1/2023.findings-emnlp.727
%P 10902-10913
Markdown (Informal)
[Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis](https://aclanthology.org/2023.findings-emnlp.727) (Chai et al., Findings 2023)
ACL