When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.

In Paradigm and Paradox, Dirk Geeraerts formulated many of the basic tenets that were to form what Cognitive Linguistics is today. Change of Paradigms -New Paradoxes links back to this seminal work, exploring which of the original theories and ideas still stand strong, which new questions have arisen and which ensuing new paradoxes need to be addressed. It thus reveals how Cognitive Linguistics has developed and diversified over the past decades.

When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read at Oxford Academic and offered as a free PDF download from OUP and selected open access locations. This book introduces a systematic framework for understanding and investigating lexical variation, using a distributional semantics approach. Distributional semantics embodies the idea that the context in which a word occurs reveals the meaning of that word. In contemporary corpus linguistics, that idea takes shape in various types of quantitative analysis of the corpus contexts in which words appear. In this book, the authors explore how count-based token-level semantic vector spaces, as an advanced form of such a quantitative methodology, can be applied to the study of polysemy, lexical variation, and lectometry. What can distributional models reveal about meaning? How can they be used to analyse the semantic relationship between near-synonyms, and to identify strict synonymy? How can they contribute to the study of lexical variation as a sociolinguistic variable, and to the use of those variables to measure convergence or divergence between language varieties? To answer these questions, the book presents a comprehensive model of lexical and semantic variation, based on the combination of a semasiological, an onomasiological, and a lectal dimension. It explains the mechanism of distributional modelling, both informally and technically, and introduces workflows and corpus linguistic tools that implement a distributional perspective in lexical research. Combining a cognitive linguistic interest in meaning with a sociolinguistic interest in variation, the authors illustrate this distributional methodology using case studies of Dutch and Spanish lexical data that focus on the detection of polysemy, the interaction of semasiological and onomasiological change, and sociolinguistic issues of lexical standardization and pluricentricity. Throughout, they highlight both the advantages and disadvantages of a distributional methodology: on the one hand, it has great potential to be scaled up for lexical research; on the other, its outcome does not necessarily neatly correspond with what would traditionally be considered different senses.