DISCIPLINARY VARIATION IN AUTOMATIC SUBLANGUAGE TERM IDENTIFICATION

The research presented here describes a method for automatically ident ifying sublanguage (SL) domain terms and revealing the patterns in whi ch they occur in text, By applying this method to abstracts from a var iety of disciplines, differences in how SL domain terminology occurs c an be discerned, Results of this research have both practical and theo retical implications, These include 1) the identification of patterns of domain term occurrence, 2) a step toward the identification of fami lies of SLs that share term occurrence patterns, 3) a domain term extr action procedure that can exploit the term occurrence patterns, and 4) evidence to support the intuitive notion of a continuum of ''technica lity'' of disciplines and their SLs, The investigation revealed relati vely consistent differences between the hard sciences, such as physics or biology, and the social sciences and humanities, such as history o r sociology. The hard sciences tended to have more domain terms, and m ore of these terms occurred in sequences than in the social sciences a nd humanities. The seed terms used in this research occurred adjacent to domain terms more often in the hard sciences than in the social sci ences, The extraction process was more successful in the hard science disciplines than in the social sciences, identifying more of the domai n terms while extracting fewer general terms.