CORPUS LINGUISTIC APPROACHES TO ENGLISH SCIENTIFIC TERMINOLOGY

Аннотация

This article explores how corpus linguistic methods contribute to the analysis and understanding of English scientific terminology. It discusses the application of frequency analysis, concordance, collocation, and semantic prosody in identifying patterns and usage of scientific terms in authentic contexts. Drawing on corpora such as the British National Corpus (BNC), the Corpus of Contemporary American English (COCA), and specialized scientific subcorpora, the study demonstrates how data-driven approaches can reveal morphological, syntactic, and semantic features of terminological units. The paper concludes that corpus linguistics plays a crucial role in terminology management, translation, lexicography, and teaching English for Specific Purposes (ESP). Keywords: corpus linguistics, scientific terminology, collocation, concordance, ESP, COCA, terminology extraction The growth of science and technology has led to a vast expansion of scientific vocabulary in English. With thousands of new terms coined annually, there is a growing need for systematic methods to analyze, classify, and teach this terminology effectively. One of the most powerful tools in this endeavor is corpus linguistics—the study of language through large collections of real-world texts, known as corpora. Corpus-based research provides insights into how scientific terms are used in context, how frequently they occur, what grammatical patterns they follow, and which words they co-occur with. These insights are valuable for various fields, including applied linguistics, translation studies, language teaching, and lexicography. This article aims to show how corpus linguistic approaches can be used to study English scientific terminology and support the development of accurate and pedagogically useful resources. To investigate scientific terminology from a corpus-linguistic perspective, the following steps and tools were used: Corpus Selection: General corpora (BNC, COCA) and domain-specific corpora (e.g., Medline for medical texts, arXiv abstracts for physics, and academic journals from the Scientific American corpus). Frequency Analysis: To identify high-frequency scientific terms and affixes. Concordance Analysis: Using AntConc software to observe usage patterns in context. www.pedagoglar.org 45-to’plam_1-qism_Iyul-2025 53 Ta'limda raqamli texnologiyalarni tadbiq etishning zamonaviy tendensiyalari va rivojlanish omillari Collocation Analysis: Measuring co-occurrence relationships (e.g., data + analysis, quantum + mechanics) using statistical measures like MI (Mutual Information) and log-likelihood. Semantic Prosody: Examining the attitudinal or connotative meaning around specific terms. Term Extraction: Using tools like Sketch Engine and WordSmith Tools to extract candidate terminology based on keyness and domain relevance. Frequency and Productivity of Scientific Terms Corpus data showed that English scientific texts are heavily loaded with nominal phrases (e.g., climate change, genetic mutation, information retrieval), many of which follow the N + N or Adj + N patterns. Top frequent terms (from COCA-academic register): data, analysis, research, study, result, model. Common affixes in terms: -logy, -scope, bio-, eco-, techno-. Emerging neologisms: infodemic, nanocarrier, biointerface. Collocational Patterns Data collocates with collection, analysis, set, processing. Gene collocates with expression, sequence, therapy, editing. These patterns reveal semantic frames and help disambiguate polysemous terms. Concordance and Contextual Analysis The term algorithm appeared in contexts such as: search algorithm, learning algorithm, encryption algorithm Often preceded by adjectives like efficient, proprietary, or complex These concordance lines indicate disciplinary variation and usage specificity in computing vs. data science contexts. Semantic Prosody The term mutation showed a predominantly negative semantic prosody in medical contexts (e.g., harmful mutation, genetic disorder), while in evolutionary biology, the tone was more neutral or positive (beneficial mutation, adaptive mutation). This variation has implications for ESP teaching and scientific communication. Genre-Specific Terminological Variation Across Disciplines Corpus data revealed that identical scientific terms may display distinct semantic and functional profiles depending on the disciplinary context. For instance: The term model in physics is often used to describe theoretical constructs (quantum model, standard model), whereas in biology it refers to experimental systems or organisms (animal model, in vitro model). www.pedagoglar.org 45-to’plam_1-qism_Iyul-2025 54 Ta'limda raqamli texnologiyalarni tadbiq etishning zamonaviy tendensiyalari va rivojlanish omillari The word interface refers to a user or system boundary in computing (user interface, software interface), but in biology or chemistry, it denotes the contact surface between substances or systems (cellular interface, protein