@inproceedings{xu-etal-2024-hitsz,
    title = "{HITSZ}-{HLT} at {SIGHAN}-2024 dim{ABSA} Task: Integrating {BERT} and {LLM} for {C}hinese Dimensional Aspect-Based Sentiment Analysis",
    author = "Xu, Hongling  and
      Zhang, Delong  and
      Zhang, Yice  and
      Xu, Ruifeng",
    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://preview.aclanthology.org/jlcl-multiple-ingestion/2024.sighan-1.20/",
    pages = "175--185",
    abstract = "This paper presents the winning system participating in the ACL 2024 workshop SIGHAN-10 shared task: Chinese dimensional aspect-based sentiment analysis (dimABSA). This task aims to identify four sentiment elements in restaurant reviews: aspect, category, opinion, and sentiment intensity evaluated in valence-arousal dimensions, providing a concise yet fine-grained sentiment description for user opinions. To tackle this task, we introduce a system that integrates BERT and large language models (LLM) to leverage their strengths. First, we explore their performance in entity extraction, relation classification, and intensity prediction. Based on preliminary experiments, we develop an integrated approach to fully utilize their advantages in different scenarios. Our system achieves first place in all subtasks and obtains a 41.7{\%} F1-score in quadruple extraction."
}