Register Studies - Volume 3, Issue 1, 2021

Abstract

This paper describes a digital curation study aimed at comparing the composition of large Web corpora, such as enTenTen, ukWac or ruWac, by means of automatic text classification. First, the paper presents a Deep Learning model suitable for classifying texts from large Web corpora using a small number of communicative functions, such as Argumentation or Reporting. Second, it describes the results of applying the automatic classification model to these corpora and compares their composition. Finally, the paper introduces a framework for interpreting the results of automatic genre classification using linguistic features. The framework can help in comparing general reference corpora obtained from the Web and in comparing corpora across languages.

Abstract

A number of studies have found that grammatical differences across registers are more extensive than those across dialects. However, there is a paucity of research examining intervarietal register change, exploring how registers change differently over time in different regional varieties. The present study addresses this diachronic deficit, focusing on grammatical developments – from the early 20th to the early 21st century – in corpora representing three written registers and two speech-based registers in Australian, British and American English. We conducted a factor analysis on 68 lexicogrammatical features to identify six dimensions of register variation, and subsequently investigated the diachronic change of the five registers across these dimensions. We interpret our findings in terms of the differential effects of broad social changes on individual registers, in light of existing findings on trends of change in different registers and varieties.

Abstract

The growing popularity of keyword analysis as an applied linguistics methodology has not been matched by an increase in the rigour with which the method is applied. While several studies have investigated the impact of choices made at certain stages of the keyword analysis process, the impact of the choice of benchmark corpus has largely been overlooked. In this paper, we compare a target corpus with several benchmark corpora and show that the keywords generated are different. We also show that certain characteristics of the keyword list and of the keywords themselves vary in relatively predictable ways depending on the benchmark corpus. These variations have implications for the choice of benchmark corpus and how the results of a keyword analysis should be interpreted. Analyzing the keywords from a comparison with a large general corpus or the keyword lists from multiple comparisons may be most appropriate for register studies.

Abstract

While there have been many studies describing L2 academic writing, most of these studies have used corpora of first year or assessment writing (Crosthwaite 2016; Weigle & Friginal 2014). The present study seeks to describe linguistic variation in L2 writing for content classes and to compare these linguistic patterns to those found in L1 writing. A multi-dimensional (MD) analysis was conducted in two corpora, BAWE and BrAWE, extracting five dimensions. The L2 corpus contained 379 texts written by Brazilian students doing part of their undergrad in the UK and the L1 corpus contained 395 texts from BAWE. The results of this study indicate that L1 and L2 writers use similar linguistic resources to convey the purpose of university registers, with the exception of case studies, designs, exercises and research reports. This linguistic variation between L1 and L2 writers might be explained by students’ interpretation of these registers’ communicative purposes.

Abstract

This article introduces a new method for grouping keywords and examines the extent to which it also allows analysts to explore the interaction of discourse and subregister. It uses the multivariate statistical technique, Multiple Correspondence Analysis, to reveal dimensions of keywords which co-occur across the texts of a corpus. These dimensions are then interpreted in terms of the discourses to which they contribute within the data, thus forming the basis of a corpus-assisted discourse analysis. The approach is demonstrated through analysis of the discourses that are used to represent Muslims and Islam in a corpus of UK national newspaper articles published on these topics spanning 2010–2019. The approach reveals an interaction between discourse and subregister, hence this article argues for the need for (corpus-assisted) discourse analysts to account for subregister as a level of meaningful variation when analysing press discourse.

This article reviews Register, genre, and style (2nd ed.)