A Corpus-Based Semantic Study of Robotics Terms - Rasulova Gulmira Xursanbek Qizi; Nasirdinov Bakhodir Abdullajon O’g’li (Goodwood Publishing, 2026)
Purpose: This study aims to identify word-formation patterns and the semantic domain distribution of contemporary robotics terminology, and to explain how these two dimensions shape the functional organization of the robotics lexical system. Research Methodology: This research employs a corpus-based functional-semantic approach to analyze 146 unique terms extracted from peer-reviewed articles published in IEEE Transactions on Robotics, IEEE Robotics and Automation Letters, and Frontiers in Robotics and AI. The data were examined based on word-formation strategies and semantic domain classification. Results: The findings indicate that compounding is the most dominant word-formation strategy (74.0%), followed by acronymy, prefixation, borrowing, suffixation, and blending. Semantically, the terms are distributed across 14 functional domains, with the five largest clusters comprising Systems, Artificial Intelligence, Manipulation, Locomotion, and Navigation. Conclusions: Contemporary robotics terminology demonstrates a systematic lexical structure characterized by the dominance of compounding and function-based semantic clustering. This reflects the need for conceptual precision and communicative efficiency in scientific robotics discourse. Limitations: The study is limited to three international journals and a relatively small dataset (146 terms), which may not fully represent the global diversity of robotics terminology. Contributions: This study contributes theoretically to applied linguistics and technical terminology studies, and practically to technical communication, translation, and the standardization of robotics terminology.