Interdisciplinary integration of economics and linguistics: Corpus-based vocabulary modelling for Financial English
Abstract
This study addresses the growing need for linguistically grounded and domain-sensitive vocabulary modelling in Financial English by integrating methods from corpus linguistics with insights from financial discourse analysis. While financial communication is central to global economic systems, the vocabulary that structures this domain remains under-examined, particularly in terms of how lexical items function across different genres such as journalism, corporate reporting, and regulatory texts. This gap is especially evident in English for Specific Purposes (ESP) instruction, where existing materials often lack empirical grounding and fail to reflect the complexity of real-world financial language use. The purpose of the study is to develop a corpus-based vocabulary model that captures the semantic, collocational, and discourse functions of Financial English vocabulary. A 878,000-word Financial English Corpus (FEC) was constructed, incorporating 220 texts from financial journalism, corporate financial reports, and institutional regulatory documents. The methodological framework includes frequency analysis, mutual information (MI)-based collocational profiling, semantic tagging using the UCREL USAS system, and qualitative concordance analysis. The results demonstrate that core financial terms such as inflation, capital, risk, and yield exhibit genre-specific frequencies, collocational partners, and discourse functions. For instance, inflation is highly evaluative in regulatory discourse, while capital is linked to strategy in corporate texts. Semantic tagging revealed a significant presence of evaluative and affective language, challenging assumptions of objectivity in financial writing. The study concludes that a corpus-based model offers a replicable and pedagogically valuable approach to specialised vocabulary. The output has practical implications for ESP instruction, terminology management, and financial NLP, contributing to a more nuanced understanding of how financial meaning is constructed in institutional contexts.
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References
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