Disciplinary variation in automatic sublanguage term identification

The research presented here describes a method for automatically identifying sublanguage (SL) domain terms and revealing the patterns in which they occur in text. By applying this method to abstracts from a variety of disciplines, differences in how SL domain terminology occurs can be discerned. Results of this research have both practical and theoretical implications. These include 1) the identification of patterns of domain term occurrence, 2) a step toward the identification of families of SLs that share term occurrence patterns, 3) a domain term extraction procedure that can exploit the term occurrence patterns, and 4) evidence to support the intuitive notion of a continuum of “technicality” of disciplines and their SLs. The investigation revealed relatively consistent differences between the hard sciences, such as physics or biology, and the social sciences and humanities, such as history or sociology. The hard sciences tended to have more domain terms, and more of these terms occurred in sequences than in the social sciences and humanities. The seed terms used in this research occurred adjacent to domain terms more often in the hard sciences than in the social sciences. The extraction process was more successful in the hard science disciplines than in the social sciences, identifying more of the domain terms while extracting fewer general terms.