International Journal of Corpus Linguistics - Online First

Registers are text varieties associated with situations of use. It has now been documented, however, that texts within registers are not situationally homogeneous — just as they are not linguistically uniform. This led Biber and Egbert (2023) to propose that linguistic variation within registers functionally corresponds to situational variation among their texts. The situational factor consistently shown to vary within registers is communicative purpose. However, previous studies have examined variability in purpose within a single register and always relied on coding schemes developed for that register. As a result, there is no single taxonomy of communicative purposes to be applied across registers. This paper proposes such a framework, offering guidance for its future adaptations to other corpora, and demonstrates how its application enables (a) analyses of functional correspondence between communicative and linguistic variation among texts of several registers; (b) register comparisons for the extent of internal variation.

The variable realisation of there+be+plural arguments (e.g. there’s a lot more Chinese people) is well-documented across English varieties, but little research has examined its use among ethnic minorities or how intersecting social factors shape its variation. This study addresses these gaps, analysing 1,549 tokens from 206 Australians of Anglo, Italian, Greek, and Chinese backgrounds, recorded in the 1970s and 2010s. The study finds an apparent-time increase in there’s during the 1970s, led by teenagers across ethnic groups, followed by broad adoption by the 2010s. Usage patterns reflect intersections of age, ethnicity, and class, with working-class Adult Anglos consistently favouring there’s. Additionally, the grammatical conditioning of (there’s) has shifted: the definiteness effect, where definite and numerical determiners (e.g. these, two) favour there’s, emerged among 1970s teenagers and has since been maintained. Changes in frequency are accompanied by shifts in linguistic and social conditioning, illustrating how variation evolves within complex sociolinguistic ecologies.

The utility of historical language databases is hindered by their complexity, text variety, and lack of register information. We investigate the feasibility of deriving linguistically motivated and reliable register predictions for unannotated, long historical texts. We fine-tune BERT-based deep learning models using register-annotated data from the Corpus of Founding Era American English and predict registers for different text parts of unannotated Eighteenth Century English Online documents. We determine the model’s effectiveness in capturing pervasive linguistic features across different text sections (e.g. beginnings vs. endings), analyzing how document internal variation affects model performance and identifying which sections are most effectively predicted. Additionally, we employ the Stable Attribution Class Explanation method to extract and compare keywords from various text parts to determine the quality of the predictions. Our findings indicate that text beginnings consistently provide more reliable classifications.