Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication - Volume 5, Issue 2, 1998

The manual acquisition of terminological material from the domain-specific text material is a very time-consuming task. Recent advances in text-processing research provide a basis for automating this task. Computer-assisted term acquisition improves both the quantity and the quality of terminological work. This paper gives a brief overview of this new approach in terminology acquisition. Three subtasks are distinguished: compilation of an electronic text corpus, extraction of terminological data, and management of terminological data. Each of the subtasks will be discussed in some detail by identifying the core problems as well as proposed solutions. As a concrete initiative in this emerging field, we present an ongoing research project at the European Academy Bolzano, which illustrates the importance of computer-assisted terminology acquisition and of the resulting steps that have been taken in recent times. The paper concludes with a summary of five selected papers which have been presented at a workshop on corpus-based terminology in Bolzano. The full papers are published in this volume and in volume 4(2) of this journal.

This paper deals with computational linguistic tools and methods for the extraction of raw material for terminological glossaries from machine-readable text. We concentrate on monolingual German term candidates, and only briefly hint at tools and procedures for the creation of bilingual glossaries.Most of the examples we use to illustrate methods and results of our work come from technical texts provided by the translation services of Daimler Chrysler AG1 and from legal texts made available by the European Academy in Bozen, Sudtirol. The Academy is working on translations of legal documents for bilingual South Tyrol, and, in this context, on the creation, upgrading, and maintenance of terminological resources.

Recently, new technologies have combined to produce a sharp increase in the availability of on-line texts and their access involves end-users with different skills. The demand for tools for information retrieval, extraction, organization and integration is becoming more and more pressing to filter relevance and sort the large number of retrieved documents. It is not the amount of information that gives the value but the access at the right time and in the most suitable form to an acceptable amount of relevant documents.Moreover the increasing availability of new information-transmission technologies demands more personal information filtering. Users are no longer interested in standard summaries and choices; they need (and most of the current user interests are on) filters that they can (directly) specify to fit their preferences and requirements. One such approach could only work on information sources dynamically adaptable to changing application needs. As a consequence the interaction between application domains and information sources needs to be stressed.Natural Language Processing (NLP) tools may be helpful in identifying relevant aspects of documents providing not only the identification of morphologic or structural properties of the texts but also stressing what are salient passages in them. The identification of subsentences capturing relevant terms may be a first step to be acquainted with specific topics of a document. The availability of a system able to capture such terms, in different languages and application domains, could optimize users ' requirements.

The role of generic lexical resources as well as specialized terminology is crucial in the design of complex dialogue systems, where a human interacts with the computer using Natural Language. Lexicon and terminology are supposed to store information for several purposes, including the discrimination of semantic-ally inconsistent interpretations, the use of lexical variations, the compositional construction of a semantic representation for a complex sentence and the ability to access equivalencies across different languages. For these purposes it is necessary to rely on representational tools that are both theoretically motivated and operationally well defined. In this paper we propose a solution to lexical and terminology representation which is based on the combination of a linguistically motivated upper model and a multilingual WordNet. The upper model accounts for the linguistic analysis at the sentence level, while the multilingual WordNet accounts for lexical and conceptual relations at the word level.

This paper attempts to clarify the types of terms which are likely to be preferred among synonyms. We regard the term referred to by the other synonyms in a dictionary as the dominant term among synonyms. More than 2,000 pairs of Japanese synonymous terms for diseases are extracted from a standard medical dictionary, and their word-structures are analysed in relation with the preferred and non-preferred terms. It was found that the terms which include person names or virus names are likely to be dominant and that the terms which include place names or suffixes are not likely to be dominant. The relation between the concept which should be conveyed by the term and the one which is actually conveyed through the word-structure is discussed.

We have studied the vocabulary of Champagne wine tasting from a linguistic point of view. Our study is based on the analysis of a corpus of texts produced by wine-tasting professionals. It deals with the meaning of linguistic units used to describe our perception during wine tasting. In this article, we present the different linguistic studies we have performed with our corpus and we propose a semantic description and a graphic representation of Champagne wine tasting terminology.