Linguistic Data Science and the English Passive - (Language, Data Science and Digital Humanities) (Hardcover)

About the Book

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.

Book Synopsis

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.

Review Quotes

"A groundbreaking study that greatly advances our understanding of the BE vs. GET passive alternation, featuring lucid writing, management of massive datasets, and masterful application of computational techniques for distinguishing between verbal and adjectival passives and quantifying constraints at unprecedented scale. Truly pioneering work for computational sociolinguistics." --Dr Richard Zimmermann, Lecturer in English Language and Linguistics, University of Manchester, UK

About the Author

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 at the English Department of the University of Heidelberg, Germany.