The Role of Artificial Intelligence in Modern-Term Formation

Abstract

The rapid expansion of scientific knowledge and digital content in the 21st century has significantly increased the demand for accurate, efficient, and scalable terminology development processes. Term formation – the creation, extraction, validation, and standardisation of specialised vocabulary – has traditionally been a manual endeavour by linguists, terminologists, and subject matter experts. However, with the growing complexity and volume of information, traditional methods are no longer sufficient to keep pace. Recent advancements in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP), machine learning, and knowledge representation, are transforming terminology science. AI-powered systems can process vast multilingual and domain-specific corpora to automatically identify candidate terms, evaluate their relevance using semantic and statistical criteria, and suggest standardised forms and multilingual equivalents. These technologies support the automation of term extraction, filtering, clustering of semantic variants, and alignment across languages and disciplines. This article explores the core applications of AI in term formation, including automatic term extraction, term validation and filtering, semantic clustering, and standardisation. It also addresses integrating AI tools in multilingual environments and constructing terminological resources compatible with ontologies and knowledge graphs. While AI introduces speed, scalability, and contextual awareness to terminology management, it also raises challenges related to accuracy, cultural and linguistic sensitivity, and algorithmic bias. The study concludes that a hybrid model – combining AI's computational capabilities with expert human judgment – offers the most promising path toward creating dynamic, inclusive, and globally coherent terminological systems.