Following the growing interest in "corpus-based" approaches to computational linguistics, a number of studies have recently appeared on the topic of automatic term recognition or extraction. Because a successful term recognition method has to be based on proper insights into the nature of terms, studies of automatic term recognition not only contribute to the applications of computational linguistics but also to the theoretical foundation of terminology. Many studies on automatic term recognition treat interesting aspects of terms, but most of them are not well founded and described. This paper tries to give an overview of the principles and methods of automatic term recognition. For that purpose, two major trends are examined, i.e. studies in automatic recognition of significant elements for indexing mainly carried out in information retrieval circles, and current research in automatic term recognition in the field of computational linguistics. Keywords Automatic term recognition, au...

Introduction Corpus-based, quantitative approaches are important to the study of terminology, because terms are, unlike words, elements which can only be recognised at the level of language fact (Kageura 1995). Despite this, the only work which takes this approach is automatic term recognition (ATR) (Bourigault 1992, Daille/Gaussier/ Lang'e 1994, Enguehard/Pantera 1994, Frantzi/Ananiadou/Tsujii 1996, Justeson/ Katz 1995, Lauriston 1994) and automatic indexing (Salton 1989)[1]. Most of the simple and straightforward quantitative characterisations of terms have already been pursued in ATR work. All ATR methods perform reasonably well, but not completely satisfactorily. In addition, we do not know which method really is better. One reason for this is that ATR work has not clarified its real target. Is it attempting to recognise all the terms in a document, in a corpus, or in a field, or a representative subset? Our standpoint is that the principal target of quantitative terminological s