密集健康叙事中的结构化声明级话语表示
Structured Claim-Level Discourse Representations for Dense Health Narratives
- Temple University（天普大学）
- University of Texas at Arlington（德克萨斯大学阿灵顿分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对社交媒体健康视频中密集交织的声明，提出结构化声明级话语分析框架，构建含1,191条标注声明的基准，发现大语言模型在主题与立场上表现好但实用画像弱，需任务分解与专门推理。
AI中文摘要：
社交媒体视频中的健康话语通常包含密集交织的声明，这些声明在简短对话片段中跨越多个主题方面、立场、证据框架和修辞功能。现有方法主要依赖粗粒度的主题级、基于情感或基于立场的表示，未能充分捕捉这种结构。我们的分析发现，每分钟平均有13.22个原子声明，这促使我们引入更丰富的声明级话语表示。我们提出了一种用于密集健康叙事中声明级话语分析的结构化框架。该框架通过将原子声明与主题方面、立场和多维实用话语属性关联的元组来建模话语。为支持这一设定，我们构建了一个涵盖四个健康领域的基准数据集，包含来自60个视频的1,191条手动标注声明。利用该框架，我们评估了不同话语上下文设置下的自动化结构化话语分析。结果表明，当前的大语言模型在主题分类和立场预测方面表现强劲，但在高维实用画像方面存在困难。我们还发现，不同的话语任务受益于不同形式的上下文推理，这表明未来的系统可能需要任务分解和专门的推理策略。
英文摘要：
Health discourse in social media videos often contains densely entangled claims spanning multiple thematic aspects, stances, evidential frames, and rhetorical functions within short conversational spans. Existing approaches largely rely on coarse topic-level, sentiment-based, or stance-oriented representations that do not adequately capture this structure. Our analysis identifies an average of 13.22 atomic claims per minute, motivating richer claim-level discourse representations. We introduce a structured framework for claim-level discourse analysis in dense health narratives. Our framework models discourse through tuples linking atomic claims with thematic aspects, stance, and multidimensional pragmatic discourse attributes. To support this setting, we construct a benchmark spanning four health domains with 1,191 manually annotated claims from 60 videos. Using this framework, we evaluate automated structured discourse analysis under different discourse context settings. Results show that current LLMs achieve strong performance on thematic categorization and stance prediction, but struggle with high-dimensional pragmatic profiling. We also find that different discourse tasks benefit from different forms of contextual reasoning, suggesting that future systems may require task decomposition and specialized inference strategies.
