Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication - Online First

In institutional settings, terminology management is essential to ensure efficient communication. Traditionally, this task has been carried out manually or through the use of corpus analysis tools. However, recent advances in generative artificial intelligence have opened new avenues for automating terminographic tasks. In this context, the use of generative models is proposed for the extraction of specialised terminology in academic institutions. Specifically, this study compares two approaches to terminology extraction. On the one hand, a corpus-based approach using the Sketch Engine tool and, on the other hand, an approach based on generative artificial intelligence. To this end, UniPDTerms was implemented — a chatbot designed with ChatGPT-4o specialised in institutional terminology of the University of Padua (Italy) and fed with an ad hoc corpus. The evaluation of both systems was performed using a reference list and analysing precision, recall, F-score and MRR metrics for each model. The results indicate that Sketch Engine and UniPDTerms performed at a comparable level under identical evaluation conditions. Although the two systems use different extraction mechanisms, their outputs produce similar results: Sketch Engine extracts relevant term candidates using frequency-based corpus analysis, whereas UniPDTerms draws on contextual and semantic relations. These results highlight the potential of incorporating generative artificial intelligence into terminographic workflows, offering new possibilities for improving efficiency and supporting end-users in terminology management.

The International Maritime Organization’s (IMO) Standard Marine Communication Phrases (SMCP) is a vital component in marine radio communication for vessel traffic service (VTS) operators and other marine personnel, playing a critical role in preventing maritime accidents caused by miscommunication. The current paper presents a Ship-shore Radio Communication Technical Vocabulary List (SRCTVL), which was developed based on an analysis of the IMO SMCP and the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA) guidance. Quantitative and qualitative approaches were used to develop the SRCTVL. The quantitative analysis involved the use of computer programs designed for corpus analysis, while the qualitative analysis consisted of collaboration with trade experts and a thorough examination of technical documents. The resulting SRCTVL contains 188 word types. Coverage evaluation demonstrates that the SRCTVL covers 25.02% of the SMCP corpus and 11.47% of an authentic ship-shore communication corpus. The SRCTVL serves as a valuable resource for maritime English instructors, facilitating targeted training for both native and non-native English-speaking bridge crews, VTS operators, and other personnel who use radio communication in VTS areas. Implementing vocabulary-based training using the SRCTVL could contribute to reducing miscommunication in multilingual workplaces, thereby enhancing overall safety at sea.

Multiword terms are units that terminologists deal with in their everyday work. In terminology, the relevance of multiword terms is argued on the basis that they label concepts in knowledge structures. However, the identification of multiword terms in texts, their specific features when compared to other multiword expressions, and their representation in terminology resources raise many questions. This article examines some problems related to the traditional recording in terminology resources which basically consists in creating separate entries for multiword terms. It then compares two alternative approaches: 1. a lexical approach that records different kinds of multiword expressions as subentries; and 2. a construction-based approach that captures sets of multiword expressions as patterns. The two methods are illustrated with environment terms. The comparison shows that the construction-based approach is very promising as that it can prevent some of the shortcomings of the other ones. A proposal for its implementation is presented.

This paper explores medical terminology translation from an ethical perspective, focusing on the Chinese translation of “Alzheimer’s disease.” Historically translated as both “老年痴呆症” and “阿尔茨海默病”, the latter is now more commonly used. The study examines the intersection of medical and translation ethics, emphasizing the balance between scientific accuracy and humanitarian concern. By tracing the term’s development in China, it reveals the complex context of terminology translation. Drawing on ethical theories, the paper highlights the need for accuracy, standardization, and ethical responsibility toward patients. It argues that medical translation goes beyond language conversion to include ethical sensitivity and respect for patient dignity. Ultimately, medical translation should prioritize precision while embracing ethical considerations to promote a humanistic spirit in healthcare communication.

This paper examines the crucial role of Western social science terminology (WSST) translation in China during the late Qing dynasty and early Republican periods through a case study of Yan Fu’s translations. Against the backdrop of profound socio-political upheavals of the time, Yan Fu’s work facilitated intellectual discourse and played a crucial role in China’s modernization. By analyzing his translation strategies, terminology choices, and the socio-cultural factors shaping his work, this study offers a comprehensive understanding of the evolution of WSST translation during the period. Through statistical analysis and a systematic exploration of terminology databases, the research highlights how Yan Fu’s translations mirrored key trends, such as the tension between foreign cultural influences, utilitarian ideals, and a rethinking of traditional Chinese values. Yan Fu’s translations serve as a window into the broader patterns of cross-cultural exchange, contributing to China’s academic development.