Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication - Volume 20, Issue 2, 2014

The paper examines the possible usage of event templates derived from Frame-Based Terminology (Faber et al. 2005, 2006, 2007) as an aid to the extraction and management of legal terminology embedded in the multi-level legal system of the European Union. The method proposed here, which combines semi-automatic term extraction and a simplified event template containing six categories, is applied to an English corpus of EU texts focusing on victims of crime and their rights. Such a combination allows for the extraction of category-relevant terminological units and additional information, which can then be used for populating a terminological knowledge base organised on the basis of the same event template, but which also employs additional classification criteria to account for the multidimensionality encountered in the corpus.

Over the past decades, English has become the predominant language for the transfer of specialized knowledge, which conditions the creation of new lexical units in other codes. This conditioning can result in terminological dependency, a linguistic phenomenon arising from a unidirectional transfer of specialized denominations between two languages. Terminological neology in potentially dependent codes such as Spanish reflects the way the importation of units from English involves a set of linguistic asymmetries that affects the conceptual configuration of specialized domains by the importing community of scientists. In this article, we propose a three-step analysis in order to detect and measure the terminological dependency of Spanish on English in the domain of Alzheimer’s Disease, based on the semantic characterization of a set of specialized neologisms related to this domain. After analysing the semantic features of these units in English, we found a significant, though not exact, correlation between the uniformity of certain semantic aspects and the degree of terminological dependency observed in their equivalents in Spanish.

The importance of semantic descriptions of concepts by means of defining statements is a commonplace tenet of scientific and practical approaches to terminology. While the current understanding of defining statements remains bound to classical concepts of defining, there is limited knowledge about the types of conceptual information that may ease the transfer of knowledge. Furthermore, there is little insight into how defining statements differ epistemologically from non-defining (generic) statements; on the linguistic side, the same can be said about linguistic differences between defining and generic statements. Last but not least, it remains unclear how practical terminology work can benefit from corpus-based research on the description of defining statements. This paper aims to shed light on some of these questions by describing a corpus-linguistic study of knowledge-rich contexts in German and Russian web corpora. Hypotheses about linguistic features of knowledge-rich contexts are derived in a theory-driven manner and researched by means of corpus-linguistic methods. Significant features are then investigated further for the German data, using a multivariate method.

While terminological relations are central in terminology work, they are rarely extensively described in large-scale terminology resources. Knowledge-rich contexts (KRCs) describing relations are commonly used in terminological analysis, but less often displayed to resources’ users, who thus lack information useful for understanding and writing in specialized fields. Using some existing resources, including the prototype resource the CREATerminal, we explore the need for terminology resources that highlight KRCs and terminological relations, potential strategies for integrating these items in resources, and initial user reactions to a KRC-enriched resource. We conclude that a smooth interface between basic term record fields and information on terminological relations in the form of KRCs, as well as easy and multi-faceted access and navigation, will help to create useful and user-friendly terminology resources particularly suited to student translators.

HypoTerm is a data-driven semantic relation finder that starts from a list of automatically extracted domain- and user-specific terms from technical corpora, and generates a list of relations between these terms. This research study focused on the detection of hypernym relations between relevant terms and named entities. In order to detect all relevant hypernym relations in technical texts, we combined a lexico-syntactic pattern-based approach and a morpho-syntactic analyzer. To evaluate our relation finder, we constructed and manually annotated gold standard data for the dredging and financial domain in Dutch and English. The experimental results show that the HypoTerm system achieves high precision and recall figures for technical texts when starting from valid domain-specific terms and named entities. Thanks to this data-driven approach, it is possible to take an important step from terminology to concept extraction without using any external lexico-semantic resources.

This paper presents an innovative approach, within the framework of distributional semantics, for the exploration of semantic similarity in a technical corpus. In complement to a previous quantitative semantic analysis conducted in the same domain of machining terminology, this paper sets out to discover fine-grained semantic distinctions in an attempt to explore the semantic heterogeneity of a number of technical items. Multidimensional scaling analysis (MDS) was carried out in order to cluster first-order co-occurrences of a technical node with respect to shared second-order and third-order co-occurrences. By taking into account the association values between relevant first and second-order co-occurrences, semantic similarities and dissimilarities between first-order co-occurrences could be determined, as well as proximities and distances on a graph. In our discussion of the methodology and results of statistical clustering techniques for semantic purposes, we pay special attention to the linguistic and terminological interpretation.