TERMINOLOGY IN THE DIGITAL AGE

Abstract

Terminology in the twenty-first century has become an interdisciplinary, evidence-driven field that integrates corpus-based methods, standardized infrastructures, and representational technologies to serve multilingual and machine-mediated communication. Large digital corpora and automated extraction algorithms enable scalable identification and diachronic analysis of candidate terms, while human expertise remains indispensable for sense disambiguation, definitional precision, and governance decisions. Versioned, machine-readable termbases and exchange formats have transformed static glossaries into interoperable knowledge artifacts that integrate with translation memories, content-management systems, and regulatory workflows, elevating issues of provenance, licensing, and stewardship. Linking terminological entries to formal schemas and ontologies enhances interoperability and automated reasoning, but mapping between representational models requires careful modeling to avoid semantic loss. Integration with neural language technologies demonstrates that terminology injection—through constrained decoding, terminology-aware fine-tuning, or post-editing—can substantially improve terminological fidelity without unduly compromising fluency when contextual constraints are respected, though gains vary by domain, language pair, and resource availability. Persistent challenges include scaling expert validation, treating genuine cross-linguistic non-equivalence, expanding coverage for less-resourced languages, and establishing reproducible, evidence-based workflows. The article advocates for shared corpora, transparent extraction and validation protocols, interoperable standards, and governance frameworks to build robust, equitable terminological infrastructures that meet scholarly inquiry and practical multilingual communication needs.