In this article I discuss the issues and challenges of compiling a corpus of historical plays by a range of playwrights that is highly suitable for use in comparative, corpus-based research into language style in Shakespeare’s plays. In discussing sources for digitised historical play-texts and criteria for making a selection for the present study, I argue that not just any set of Early Modern English plays constitutes a suitable basis upon which to make reliable claims about language style in Shakespeare’s plays relative to those of his peers. I point out factors outside of authorial choice which potentially have bearing on language style, such as sub-genre features and change over time. I also highlight some particular difficulties in compiling a corpus of historical texts, notably dating and spelling variation, and I explain how these were addressed. The corpus detailed in this article extends the prospects for investigating Shakespeare’s language style by providing a context into which it can be set and, as I indicate, is a valuable new publicly accessible resource for future research.
Corpus-based studies of learner language and (especially) English varieties have become more quantitative in nature and increasingly use regression-based methods and classifiers such as classification trees, random forests, etc. One recent development more widely used is the MuPDAR (Multifactorial Prediction and Deviation Analysis using Regressions) approach of Gries and Deshors (2014) and Gries and Adelman (2014). This approach attempts to improve on traditional regression- or tree-based approaches by, firstly, training a model on the reference speakers (often native speakers (NS) in learner corpus studies or British English speakers in variety studies), then, secondly, using this model to predict what such a reference speaker would produce in the situation the target speaker is in (often non-native speakers (NNS) or indigenized-variety speakers). Crucially, the third step then consists of determining whether the target speakers made a canonical choice or not and explore that variability with a second regression model or classifier.
Both regression-based modeling in general and MuPDAR in particular have led to many interesting results, but we want to propose two changes in perspective on the results they produce. First, we want to focus attention on the middle ground of the prediction space, i.e. the predictions of a regression/classifier that, essentially, are made non-confidently and translate into a statement such as ‘in this context, both/all alternants would be fine’. Second, we want to make a plug for a greater attention to misclassifications/-predictions and propose a method to identify those as well as discuss what we can learn from studying them. We exemplify our two suggestions based on a brief case study, namely the dative alternation in native and learner corpus data.
This paper presents a newly-compiled diachronic corpus of Australian English (AusBrown). With four sampling time points (1931, 1961, 1991 and 2006), Aus-Brown is designed to match the current suite of British and American ‘Brown-family’ corpora in both sampling year and design. We provide details of the composition and compilation of AusBrown, and explore the broader context of its ‘Brown-family background’ and of complementary Australian corpora. We also overview research based on the Australian corpora presented, including several AusBrown-based papers.
The present article provides a detailed description of the corpus of Early Modern Multiloquent Authors (EMMA), as well as two small case studies that illustrate its benefits. As a large-scale specialized corpus, EMMA tries to strike the right balance between big data and sociolinguistic coverage. It comprises the writings of 50 carefully selected authors across five generations, mostly taken from the 17th-century London society. EMMA enables the study of language as both a social and cognitive phenomenon and allows us to explore the interaction between the individual and aggregate levels.
The first part of the article is a detailed description of EMMA’s first release as well as the sociolinguistic and methodological principles that underlie its design and compilation. We cover the conceptual decisions and practical implementations at various stages of the compilation process: from text-markup, encoding and data preprocessing to metadata enrichment and verification.
In the second part, we present two small case studies to illustrate how rich contextualization can guide the interpretation of quantitative corpus-linguistic findings. The first case study compares the past tense formation of strong verbs in writers without access to higher education to that of writers with an extensive training in Latin. The second case study relates s/th-variation in the language of a single writer, Margaret Cavendish, to major shifts in her personal life.
In this article we would like to examine an area of onomastics that has not received much scholarly attention. We aim to provide an adequate linguistic analysis of the place-names found in The Elder Scrolls (ES) video game series. For our analysis, we rely chiefly on the methods of linguistic statistics, which have not yet gained widespread use in onomastic research. Our goal is to give a boost to linguistic and onomastic research into video games and to develop related aspects of its research methodology. Two main methods of place-name formation can be observed in our results: one is when the fictional names are coined on the basis of the lexical elements of already existing non-fictional languages (we call these mimetic names), and the other is when the game developers create so-called speaking names. In our article we demonstrate that the toponyms of the ES universe in part conform to the conventions of non-fictional place-name formation (e.g. they can be sorted into the two main categories of habitative names and topographical names), and in part they contradict such conventions, because around 14 percent of the names we analyzed are purposefully coined as semantically obscure toponyms, which does not happen in the case of non-fictional names.