@inproceedings{a74169ce268b4591ac273491f76394f4,
title = "DS-Group at SIGHAN-2024 dimABSA Task: Constructing In-context Learning Structure for Dimensional Aspect-Based Sentiment Analysis",
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. Our code is publicly available at: https://github.com/Maydayflower/dimABSA-ICL.",
author = "Meng, {Ling Ang} and Tianyu Zhao and Dawei Song",
note = "Publisher Copyright: {\textcopyright} 2024 Association for Computational Linguistics; 10th SIGHAN Workshop on Chinese Language Processing, SIGHAN 2024 ; Conference date: 16-08-2024",
year = "2024",
language = "English",
series = "SIGHAN 2024 - 10th SIGHAN Workshop on Chinese Language Processing, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "127--132",
editor = "Kam-Fai Wong and Min Zhang and Ruifeng Xu and Jing Li and Zhongyu Wei and Lin Gui and Bin Liang and Runcong Zhao",
booktitle = "SIGHAN 2024 - 10th SIGHAN Workshop on Chinese Language Processing, Proceedings of the Workshop",
address = "United States",
}