Modeling Professionalism in Expert Questioning through Linguistic Differentiation

Giulia D'Agostino; Chung-Chi Chen

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arXiv · 2507.20249

Modeling Professionalism in Expert Questioning through Linguistic Differentiation

Giulia D'Agostino·Chung-Chi Chen

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Abstract

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.

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Giulia D'Agostino, Chung-Chi Chen. 2025-07-27. Modeling Professionalism in Expert Questioning through Linguistic Differentiation. https://arxiv.org/abs/2507.20249

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