Abstract
Current term recognition algorithms havecentred mostly on the notion of term based onthe assumption that terms are monoreferentialand as such independent of context. Thecharacteristics and behaviour of terms in realtexts are however far removed from this idealbecause factors such as text type orcommunicative situation greatly influence thelinguistic realisation of a concept. Context,therefore, is important for the correctidentification of terms (Dubuc and Lauriston,1997). Based on this assumption, we haveshifted our emphasis from terms towardssurrounding linguistic context, namely verbs,as verbs are considered the central elements inthe sentence. More specifically, we have setout to examine whether verbs and verbal syntaxin particular, could help us towards the taskof automatic term recognition. Our findingssuggest that term occurrence variessignificantly in different argument structuresand different syntactic positions. Additionally, deviant grammatical structureshave proved rich environments for terms. Theanalysis was carried out in three differentspecialised subcorpora in order to explore howthe effectiveness of verbal syntax as apotential indicator of term occurrence can beconstrained by factors such as subject matterand text type.
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Eumeridou, E., Nkwenti-Azeh, B. & McNaught, J. An Analysis of Verb Subcategorization Frames in Three Special Language Corpora with a View towards Automatic Term Recognition. Computers and the Humanities 38, 37–60 (2004). https://doi.org/10.1023/B:CHUM.0000009278.73498.f4
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DOI: https://doi.org/10.1023/B:CHUM.0000009278.73498.f4