@inproceedings{meng-etal-2024-ds,
title = "{DS}-Group at {SIGHAN}-2024 dim{ABSA} Task: Constructing In-context Learning Structure for Dimensional Aspect-Based Sentiment Analysis",
author = "Meng, Ling-ang and
Zhao, Tianyu and
Song, Dawei",
editor = "Wong, Kam-Fai and
Zhang, Min and
Xu, Ruifeng and
Li, Jing and
Wei, Zhongyu and
Gui, Lin and
Liang, Bin and
Zhao, Runcong",
booktitle = "Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sighan-1.15",
pages = "127--132",
abstract = "Aspect-Based Sentiment Analysis (ABSA) is an important subtask in Natural Language Processing (NLP). More recent research within ABSA have consistently focused on conducting more precise sentiment analysis on aspects, i.e., dimensional Aspect-Based Sentiment Analysis (dimABSA). However, previous approaches have not systematically explored the use of Large Language Models (LLMs) in dimABSA. To fill the gap, we propose a novel In-Context Learning (ICL) structure with a novel aspect-aware ICL example selection method, to enhance the performance of LLMs in dimABSA. Experiments show that our proposed ICL structure significantly improves the fine-grained sentiment analysis abilities of LLMs.",
}
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%0 Conference Proceedings
%T DS-Group at SIGHAN-2024 dimABSA Task: Constructing In-context Learning Structure for Dimensional Aspect-Based Sentiment Analysis
%A Meng, Ling-ang
%A Zhao, Tianyu
%A Song, Dawei
%Y Wong, Kam-Fai
%Y Zhang, Min
%Y Xu, Ruifeng
%Y Li, Jing
%Y Wei, Zhongyu
%Y Gui, Lin
%Y Liang, Bin
%Y Zhao, Runcong
%S Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F meng-etal-2024-ds
%X Aspect-Based Sentiment Analysis (ABSA) is an important subtask in Natural Language Processing (NLP). More recent research within ABSA have consistently focused on conducting more precise sentiment analysis on aspects, i.e., dimensional Aspect-Based Sentiment Analysis (dimABSA). However, previous approaches have not systematically explored the use of Large Language Models (LLMs) in dimABSA. To fill the gap, we propose a novel In-Context Learning (ICL) structure with a novel aspect-aware ICL example selection method, to enhance the performance of LLMs in dimABSA. Experiments show that our proposed ICL structure significantly improves the fine-grained sentiment analysis abilities of LLMs.
%U https://aclanthology.org/2024.sighan-1.15
%P 127-132
Markdown (Informal)
[DS-Group at SIGHAN-2024 dimABSA Task: Constructing In-context Learning Structure for Dimensional Aspect-Based Sentiment Analysis](https://aclanthology.org/2024.sighan-1.15) (Meng et al., SIGHAN-WS 2024)
ACL