Modeling Professionalism in Expert Questioning through Linguistic Differentiation
This work addresses the underexplored dimension of professionalism in expert communication for domains like finance, though it is incremental as it builds on existing linguistic analysis methods.
This paper tackled the problem of modeling professionalism in expert questioning by analyzing linguistic features in financial analyst questions, showing that a classifier based on these features outperforms baselines like gemini-2.0 and SVM in distinguishing expert-authored questions.
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.