The contribution of verbal semantic content towards term recognition
Automatic term recognition is a natural language processing technology which is gaining increasing prominence in our information-overloaded society. Apart from its use for quick and efficient updating of terminologies and thesauri, it has also been used for machine translation, information retrieval, document indexing and classification as well as content representation. Until very recently, term identification techniques rested solely on the mapping of term linguistic properties onto computational procedures. However, actual terminological practice has shown that context is also important for term identification and interpretation as terms may appear in different forms depending on the situation of use. The aim of this article is to show the importance of contextual information for automatic term recognition by exploiting the relation between verbal semantic content and term occurrence in three subcorpora drawn from the British National Corpus.
Keywords: special language corpora, special languages, verbal semantic content, selectional restrictions, WordNet, automatic term recognition
Published online: 18 October 2002