通过语言差异化建模专家提问中的专业性
Modeling Professionalism in Expert Questioning through Linguistic Differentiation
AI总结：
针对金融专家提问的专业性评估问题，本文提出语言标注框架并构建两类数据集，证明可解释语言特征能有效识别人类专家问题并超越基线模型。
摘要：
专业性是专家沟通中一个至关重要但尚未得到充分探索的维度，尤其是在金融等高风险领域。本文研究如何利用语言特征来建模和评估专家提问中的专业性。我们提出了一种新颖的标注框架，用于量化金融分析师问题中的结构性和语用要素，例如话语调节成分、前言和请求类型。利用人工撰写和大语言模型（LLM）生成的问题，我们构建了两个数据集：一个标注了感知到的专业性，另一个按问题来源进行标注。我们表明，相同的语言特征与人类判断和作者来源都具有很强的相关性，这表明存在共同的风格基础。此外，仅基于这些可解释特征训练的分类器在区分专家撰写的问题方面优于 gemini-2.0 和 SVM 基线。我们的研究结果表明，专业性是一种可学习的、跨领域通用的构念，可以通过基于语言的建模来捕捉。
英文摘要：
Professionalism is a crucial yet underexplored dimension of expert communication, particularly in high-stakes domains like finance. This paper investigates how linguistic features can be leveraged to model and evaluate professionalism in expert questioning. We introduce a novel annotation framework to quantify structural and pragmatic elements in financial analyst questions, such as discourse regulators, prefaces, and request types. Using both human-authored and large language model (LLM)-generated questions, we construct two datasets: one annotated for perceived professionalism and one labeled by question origin. We show that the same linguistic features correlate strongly with both human judgments and authorship origin, suggesting a shared stylistic foundation. Furthermore, a classifier trained solely on these interpretable features outperforms gemini-2.0 and SVM baselines in distinguishing expert-authored questions. Our findings demonstrate that professionalism is a learnable, domain-general construct that can be captured through linguistically grounded modeling.
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。
