RT Conference Proceedings
T1 A Comparative Analysis of Large Language Models for Bilingual Term Extraction in Spanish-Arabic Interpreting and Translation
A1 Gaber, Mahmoud
K1 Inteligencia artificial
K1 Traducción automática
AB The burgeoning capabilities of Large Language Models (LLMs) are profoundly impacting Natural Language Processing (NLP), with their application in terminology extraction gaining increasing scholarly attention [4]. Terminology extraction is a cornerstone of professional interpreting and translation, indispensable for upholding semantic precision and discursive fluency in specialised communication [1]. Automatic Terminology Extraction (ATE) methodologies endeavour to mitigate the arduous demands of manual terminology management by generating ranked lists of candidate terms from domain-specific corpora [2]. Despite the remarkable capabilities of LLMs, often attributed to their sophisticated training paradigms [4], empirical evaluations of these models for ATE purposes remain notably scarce, particularly concerning linguistically divergent pairs such as Spanish-Arabic. Existing research predominantly focuses on European language combinations, thereby creating a critical lacuna in understanding AI's efficacy in non-Indo-European linguistic contexts. Given the inherent structural and semantic disparities between Spanish and Arabic, a comprehensive assessment of AI's performance in this domain is imperative for ensuring the reliability of LLM-driven tools in professional linguistic workflows [5]. This study undertakes a comparative evaluation of AI tools—specifically ChatGPT, DeepSeek, Gemini, and Manus—for their proficiency in extracting bilingual Spanish-Arabic terminology across two distinct specialised domains: ophthalmology (medical) and tourism. Utilising a methodological framework encompassing Precision, Recall, F-score, and Accuracy [3], this research provides a granular assessment of each tool's capacity for accurate and contextually relevant bilingual term identification. The findings contribute to the theoretical and practical advancements in AI-assisted terminology extraction, offering insights into the evolving landscape of AI integration within interpreting and translation studies.
YR 2025
FD 2025-10-29
LK https://hdl.handle.net/10630/45386
UL https://hdl.handle.net/10630/45386
LA eng
NO Instituto Universitario de Investigación de Tecnologías Lingüísticas Multilingües
NO Universidad de Málaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 mar 2026