用统一语义表示提升零样本方面分类情感分析效果
Exploring Zero-Shot ACSA with Unified Meaning Representation in Chain-of-Thought Prompting
- 引入统一语义表示构建思维链，增强大模型推理逻辑
- 中等规模模型（如Qwen3-8B）在多个数据集上表现与基线相当
- 适合资源有限场景下快速部署情感分析系统
方面-类别情感分析（ACSA）通过识别评论中的具体主题及其情感倾向，提供细粒度洞察。尽管监督学习方法主导该领域，但新领域标注数据稀缺且成本高昂，构成显著障碍。我们主张在数据标注资源有限时，利用大语言模型进行零样本推理是一种可行替代方案。本文提出一种新型思维链（CoT）提示方法，通过中间统一语义表示（UMR）结构化任务推理过程。我们在三个模型（Qwen3-4B、Qwen3-8B、Gemini-2.5-Pro）和四个不同数据集上评估该方法。结果表明，UMR效果可能依赖于模型规模：初步结果显示，中等规模模型（如Qwen3-8B）性能与标准CoT基线相当，但需进一步研究其在小型模型架构上的适用性，当前结论的普适性仍有待验证。
原文摘要 · Abstract (English)
Aspect-Category Sentiment Analysis (ACSA) provides granular insights by identifying specific themes within reviews and their associated sentiment. While supervised learning approaches dominate this field, the scarcity and high cost of annotated data for new domains present significant barriers. We argue that leveraging large language models (LLMs) in a zero-shot setting is a practical alternative where resources for data annotation are limited. In this work, we propose a novel Chain-of-Thought (CoT) prompting technique that utilises an intermediate Unified Meaning Representation (UMR) to structure the reasoning process for the ACSA task. We evaluate this UMR-based approach against a standard CoT baseline across three models (Qwen3-4B, Qwen3-8B, and Gemini-2.5-Pro) and four diverse datasets. Our findings suggest that UMR effectiveness may be model-dependent. Whilst preliminary results indicate comparable performance for mid-sized models such as Qwen3-8B, these observations warrant further investigation, particularly regarding the potential applicability to smaller model architectures. Further research is required to establish the generalisability of these findings across different model scales.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。