Employs a combination of machine-learning tools to study the choice of English passive auxiliary, BE vs GET, in several large corpora, both diachronically and across varieties.
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Axel Bohmann is Professor of English Linguistics at the University of Cologne, Germany. His work combines corpus linguistics, variationist sociolinguistics, and discourse analysis, often in the context of English in global settings. He holds a PhD in English from the University of Texas at Austin and has published his research in journals such as English Worldwide, the Journal of Sociolinguistics, and Language.
Julia Müller is a postdoctoral researcher at the English Department of the University of Freiburg, Germany. She studies second-language processing using experimental methods and is also passionate about accessible programming, statistics, and data science education.
Mirka Honkanen worked as a postdoctoral researcher at the University of Freiburg, Germany, and now pursues a career in science management and administration.
Miriam Neuhausen is Assistant Professor of English Linguistics at Heidelberg University, Germany. Her work explores language, identity, and group membership through a sociophonetic lens. She is also interested in lesser-studied varieties/communities and the link between language and social injustice.
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Hardcover. Etat : new. Hardcover. The choice between BE and GET as auxiliary verbs, as in She was promoted vs She got promoted, is a central, grammatical feature, yet the many proposed nuances conditioning this phenomenon have escaped large-scale empirical validation to date. This book fills this gap, using multivariate statistical analyses of several large corpora to explore different factors determining the choice of English passive auxiliary.Addressing both diachronic developments (using the Corpus of Historical American English) and synchronic regional variation (using the Corpus of Global Web-based English), the book employs methods that combine traditional corpus linguistics with newer machine-learning tools in an innovative and intricate manner. To circumscribe the variable context, the authors train a statistical model to distinguish central from peripheral passives. The study tests the influence of various predictors, derived from the previous literature on the passive, with the use of automated sentiment analysis and subject detection, manual animacy coding, distributional semantics, and a mixed-effects regression model. Putting forward an automatic way of distinguishing more stative from more dynamic passives, the book demonstrates how to examine the passive construction in a much larger dataset than in previous studies, and shows how advanced computational models can be used to productively engage traditional philological questions, such as those related to language change and regional variation. Employs a combination of machine-learning tools to study the choice of English passive auxiliary, BE vs GET, in several large corpora, both diachronically and across varieties. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781350386549
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