Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication - Volume 13, Issue 2, 2007

The diffusion and transfer of knowledge is central to the process of innovation and is of importance to both academics and industrialists. In this paper, we propose that academic articles and patent documents referring to a set of well-researched concepts may be used as a measure of this diffusion and transfer. Concepts are typically articulated as terms, and shared terms in specialist research papers and patent documents are proposed as a monitoring index for the transfer of academic knowledge to patented technology. The identification and statistical monitoring of changes in the use of terms, through the medium of texts, may provide innovative opportunities and reduce the extensive lead-time from invention to innovation. A text-based approach has been taken to research the changes in terminology that occurred in the development of Artificial Intelligence since 1936. Biological models of growth have been applied to model the diachronic changes, and the results show that the growth of term usage may be modelled by using logistic growth techniques.

This paper aims at understanding the reasons why variants of the derivatives of HAEMA — Greek for blood — occur in medical vocabulary. Aspects of variation are seen from a terminological standpoint, which may differ from that prevailing in lexicography or in natural language processing (NLP). We consider as variants of a term not only the forms that are spelt in a similar way, but also its synonyms, i.e. all the forms that have a similar definition. However, since hyponyms of a given term differ by their definitions, i.e. have a separate meaning, we treat them as separate terms: variation becomes part of term formation. The examples in English come from medical dictionaries, a glossary in haematology and MEDLINE. Several types of variation are analysed: graphic, morphological, morphosyntactic, syntactic and lexical, together with their possible meanings, and case studies of the use of some of the variants in context are presented.

The aim of the study described in this article is to examine the lexical characteristics of medical research articles in English. The article describes the results obtained from the analysis of a corpus of medical texts in which the use of verbs is studied in the different sections of the medical research article. After selecting a corpus of 30 texts and tagging them with a POS Tagger, concordances of verbs were obtained. The verbs were then classified according to their meaning in lexical domains. Results show that the lexical domains are distributed differently in each section of the article, reinforcing the rhetorical functions of each section. A proposal is also made for using these results in a more practical manner, for example to enrich an electronic manual, with information that could assist professionals and translators with the writing and translation of medical research articles.

Understanding the semantic relations between terms in specialized texts is of critical importance in translation and terminology, and generally speaking in learning from texts. Our research highlights the advantages of formalizing them in order to build hierarchies and sets of horizontal conceptual relations (i.e. process-oriented relations) for knowledge acquisition. This paper discusses a method for extracting domain-specific semantic relations in specialized texts. Obviously, some texts are more appropriate than others in this regard. ‘Knowledge-rich’ texts such as encyclopaedia and textbooks are considered good materials because of the density and richness of thematic information. Considering them as such, we used the encyclopaedic articles of the Dictionnaire analytique de la distribution/Analytical Dictionary of Retailing. We retrieved over 3000 terms semantically related to all 350 headwords of the Dictionary, and grouped them into 28 classes of relations (paradigmatic, i.e. generic, specific, agent, goal, instrument, recipient, location, etc., and also syntagmatic, such as related verbs and adjectives). This paper discusses in particular the generic, agent and property relations and examines the linguistic markers that permit their retrieval.

Term extraction may be defined as a text mining activity whose main purpose is to obtain all the terms included in a text of a given domain. Since the eighties, and mainly due to the rapid scientific advances as well as the evolution of the communication systems, there has been a growing interest in obtaining the terms found in written documents. A number of techniques and strategies have been proposed for satisfying this requirement. At present it seems that term extraction has reached a maturity stage. Nevertheless, many of the systems proposed fail to qualitatively present their results, almost every system evaluates its abilities in an ad hoc manner (if any, many times). Often, the authors do not explain their evaluation methodology; therefore comparisons between different implementations are difficult to draw. In this paper, we review the state-of-the-art of term extraction systems evaluation in the framework of natural language systems evaluation. The main approaches are presented, with a focus on their limitations. As an instantiation of some ideas for overcoming these limitations, the evaluation framework is applied to YATE, a hybrid term extractor.

In this article, we present the current state of our work on a linguistically-motivated model for automatic summarization of medical articles in Spanish. The model takes into account the results of an empirical study which reveals that, on the one hand, domain-specific summarization criteria can often be derived from the summaries of domain specialists, and, on the other hand, adequate summarization strategies must be multidimensional, i.e., cover various types of linguistic clues. We take into account the textual, lexical, discursive, syntactic and communicative dimensions. This is novel in the field of summarization. The experiments carried out so far indicate that our model is suitable to provide high quality summarizations.