Linguistic Data Science and the English Passive

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A01=Axel Bohmann
A01=Julia Muller
A01=Miriam Neuhausen
A01=Mirka Honkanen
Author_Axel Bohmann
Author_Julia Muller
Author_Miriam Neuhausen
Author_Mirka Honkanen
Auxiliary Verbs
Category=CFX
Category=GPH
Computational Linguistics
Corpora
Corpus Linguistics
Corpus of Global Web-based English
Corpus of Historical American English
Digital Humanities
eq_bestseller
eq_dictionaries-language-reference
eq_isMigrated=1
eq_isMigrated=2
eq_new_release
eq_nobargain
eq_non-fiction
Grammar
Machine-Learning
Passive Auxiliary

Product details

  • ISBN 9781350386549
  • Weight: 920g
  • Dimensions: 152 x 236mm
  • Publication Date: 11 Dec 2025
  • Publisher: Bloomsbury Publishing PLC
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

Axel Bohmann is Professor of English Linguistics at the University of Cologne, Germany.

Julia Müller is a postdoctoral researcher at the English Department of the University of Freiburg, Germany.

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.

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