APPROACHES TO LEARNING ECONOMIC TERMINOLOGY THROUGH CORPUS RESEARCH AND DIGITAL EXERCISES

This article addresses several key aspects of a recent project on the digitization of lexical resources with economic terminology—extraction, compilation, and creation of a core corpus and ten specific topical areas, software processing, creation of interactive exercises, and testing the efficiency of the learning curve in both native and foreign learners of Bulgarian and English. We used Sketch Engine for the selection, analysis of the semantic relations, behavior of lexemes, terminology database, word forms, collocations, and concordances. The bespoke corpus provided a current linguistic view of the use of economic lexemes in contemporary economic research consisting of doctoral theses, articles, and textbooks in Bulgarian and English. In the next stage, we created interactive exercises and tested their efficiency during independent learning of vocabulary items (both Bulgarian and English) in specific target groups of students – foreign students learning Bulgarian as a second language, or Bulgarian students of economics enrolled in a Business English course. As an empirical experiment, we tested the hypothesis of whether encyclopedic knowledge of economic terms acquired through definitions and examples from textbooks can be complemented, and even surpassed, by a learning approach that explains terminology in a more engaging and lively reiteration of similar content selected from representative corpora. This project offers a substantial database of lexical terms, uniting both current corpus research and didactic approaches to the acquisition of fundamental vocabulary across economic disciplines.

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Publication Details

Journal
Trakia Journal of Sciences
Published
2025-10-20
DOI
https://doi.org/10.15547/tjs.2025.s.02.006
Primary Topic
Second Language Acquisition and Learning
Type
article
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article

APPROACHES TO LEARNING ECONOMIC TERMINOLOGY THROUGH CORPUS RESEARCH AND DIGITAL EXERCISES

Tanya Taneva, N. Penev, Zh. Gundasheva, N. Ileva et al.
Trakia Journal of Sciences
Second Language Acquisition and Learning
article

APPROACHES TO LEARNING ECONOMIC TERMINOLOGY THROUGH CORPUS RESEARCH AND DIGITAL EXERCISES

Tanya Taneva, N. Penev, Zh. Gundasheva, N. Ileva, L. Spasovat, K. Katsarova, Z. Dimova
article en

Abstract

This article addresses several key aspects of a recent project on the digitization of lexical resources with economic terminology—extraction, compilation, and creation of a core corpus and ten specific topical areas, software processing, creation of interactive exercises, and testing the efficiency of the learning curve in both native and foreign learners of Bulgarian and English. We used Sketch Engine for the selection, analysis of the semantic relations, behavior of lexemes, terminology database, word forms, collocations, and concordances. The bespoke corpus provided a current linguistic view of the use of economic lexemes in contemporary economic research consisting of doctoral theses, articles, and textbooks in Bulgarian and English. In the next stage, we created interactive exercises and tested their efficiency during independent learning of vocabulary items (both Bulgarian and English) in specific target groups of students – foreign students learning Bulgarian as a second language, or Bulgarian students of economics enrolled in a Business English course. As an empirical experiment, we tested the hypothesis of whether encyclopedic knowledge of economic terms acquired through definitions and examples from textbooks can be complemented, and even surpassed, by a learning approach that explains terminology in a more engaging and lively reiteration of similar content selected from representative corpora. This project offers a substantial database of lexical terms, uniting both current corpus research and didactic approaches to the acquisition of fundamental vocabulary across economic disciplines.

Trakia Journal of SciencesVol. 23(Supplement 2)
Trakia University (BG)
Openalex Percentile: Top 64%
Second Language Acquisition and Learning
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