面向利用语言模型进行细粒度观点分析的时序知识库构建
Towards Temporal Knowledge-Base Creation for Fine-Grained Opinion Analysis with Language Models
AI总结：
针对现有文本观点分析缺乏时间锚点细粒度标注的问题，提出一种以LLM为自动标注器的可扩展时序观点知识库构建方法，定义三类数据模型，经定量评估和双模型标注验证，可适配RAG等多类下游应用。
摘要：
我们提出了一种可扩展的方法，以大语言模型（LLMs）作为自动标注器来构建时序观点知识库。尽管文本的时间序列观点分析已被证明可用于预测、趋势分析等下游应用，但由于缺乏带时间锚点的细粒度标注，现有方法未能充分挖掘这一潜力。我们的方法将成熟的观点挖掘范式整合到声明式LLM标注流水线中，无需手动设计提示词即可实现结构化观点提取，从而填补了这一空白。我们基于情感与观点挖掘文献定义了三种数据模型，作为结构化表示的模式。我们使用人工标注的测试样本对流水线进行了严格的定量评估。我们使用两个独立的LLM完成最终标注，并参照人工标注协议，在细粒度观点维度上按标签计算了标注者间一致性。最终得到的知识库封装了时间对齐的结构化观点，可适配检索增强生成（RAG）、时序问答和时间线摘要等应用。
英文摘要：
We propose a scalable method for constructing a temporal opinion knowledge base with large language models (LLMs) as automated annotators. Despite the demonstrated utility of time-series opinion analysis of text for downstream applications such as forecasting and trend analysis, existing methodologies underexploit this potential due to the absence of temporally grounded fine-grained annotations. Our approach addresses this gap by integrating well-established opinion mining formulations into a declarative LLM annotation pipeline, enabling structured opinion extraction without manual prompt engineering. We define three data models grounded in sentiment and opinion mining literature, serving as schemas for structured representation. We perform rigorous quantitative evaluation of our pipeline using human-annotated test samples. We carry out the final annotations using two separate LLMs, and inter-annotator agreement is computed label-wise across the fine-grained opinion dimensions, analogous to human annotation protocols. The resulting knowledge base encapsulates time-aligned, structured opinions and is compatible with applications in Retrieval-Augmented Generation (RAG), temporal question answering, and timeline summarisation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
