Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication - Volume 27, Issue 2, 2021

In this paper, we address the system evaluation issue for commercial term extraction tools from the users’ perspective. We first revisit the gold standard approach commonly practised among researchers, and discuss the challenges it may pose on end users, taking translators as a typical example. Considering the very different motivations and needs of users and researchers, a user-driven approach is proposed as a variation and alternative to the gold standard approach to allow users to assess and understand the performance of commercial tools more objectively. Its feasibility and usefulness are demonstrated by deploying a benchmarking dataset of English-Chinese financial terms, produced by multiple annotators, in a case study with SDL MultiTerm Extract. The results also provide insight for future development of term extractors designed for translators, which will hopefully generate more accurate candidates, offer more customised features, enable better user experience, and enjoy wider popularity as a computer-aided translation tool.

This paper analyzes nested-abbreviated terms from a linguistic perspective by describing their morphological, syntactic, and semantic features for terminology purposes. Nested-abbreviated terms can be considered as abbreviated forms, either initialisms or acronyms, which have within their meaning another abbreviated term. To carry out the analysis, 433 nested-abbreviated terms were extracted from two specialized dictionaries in English. Data analysis showed that, from the morphological and semantic perspective, nested-abbreviated terms behave like typical abbreviations. Important differences were found from a syntactic standpoint where nested abbreviated terms behave as premodifiers in the noun phrase (NP) in 98.93% of the cases. As this is the first time nested-abbreviated terms are studied, they were not only described but also analyzed and defined. Although the percentage of nested-abbreviated terms obtained from the dictionaries is relatively low, less than 1% of total abbreviations, it was found that it is highly relevant to study this growing phenomenon in specialized languages for terminology extraction, as well as for other purposes.

Automatic term extraction (ATE) is an important task within natural language processing, both separately, and as a preprocessing step for other tasks. In recent years, research has moved far beyond the traditional hybrid approach where candidate terms are extracted based on part-of-speech patterns and filtered and sorted with statistical termhood and unithood measures. While there has been an explosion of different types of features and algorithms, including machine learning methodologies, some of the fundamental problems remain unsolved, such as the ambiguous nature of the concept “term”. This has been a hurdle in the creation of data for ATE, meaning that datasets for both training and testing are scarce, and system evaluations are often limited and rarely cover multiple languages and domains. The ACTER Annotated Corpora for Term Extraction Research contain manual term annotations in four domains and three languages and have been used to investigate a supervised machine learning approach for ATE, using a binary random forest classifier with multiple types of features. The resulting system (HAMLET Hybrid Adaptable Machine Learning approach to Extract Terminology) provides detailed insights into its strengths and weaknesses. It highlights a certain unpredictability as an important drawback of machine learning methodologies, but also shows how the system appears to have learnt a robust definition of terms, producing results that are state-of-the-art, and contain few errors that are not (part of) terms in any way. Both the amount and the relevance of the training data have a substantial effect on results, and by varying the training data, it appears to be possible to adapt the system to various desired outputs, e.g., different types of terms. While certain issues remain difficult – such as the extraction of rare terms and multiword terms – this study shows how supervised machine learning is a promising methodology for ATE.

Dried meats is an area that has not been widely studied from a terminological approach despite the growing need of Spanish companies to adapt data about their products into English to export their goods abroad. In this paper, we focus on the design and compilation of e-DriMe, a Spanish and English e-dictionary intended to assist to communicate effectively in the field of dried-meats. This e-dictionary is based on the principles of the Function Theory of Lexicography (Bergenholtz and Tarp 2002, 2003) and lexical semantics for terminology (L’Homme 2020). Firstly, the methodology to compile the e-dictionary is described, which relies on the content of a virtual Spanish-English comparable corpus of dried meat product cards. In addition, term extraction and entry tailoring are explained. Finally, some entries are exemplified. To summarize, we propose a new resource, e-DriMe, that can be easily integrated into computerized writing aids and computer-assisted translation tools.

This article looks at so-called “deviant” functioning in terminology. The notion of deviancy seems to be situated in relation to a “neutral” functioning of the language, which does not take any particular communication situation into account. The article aims to show that this supposed deviancy has to be related to the communication situation itself, which, in the present case, implies specialised knowledge. Rather than just being deviancies, it is argued that these linguistic formulations are a tangible manifestation of the specificity of the communication situation. Three types of explanation are put forward for their use: linguistic (linguistic prolixity and linguistic economy), sociolinguistic, and cognitive. Each type is exemplified by various studies.