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This book is an innovative guide to quantitative, corpus-based research in historical and diachronic linguistics. Gard B. Jenset and Barbara McGillivray argue that, although historical linguistics has been successful in using the comparative method, the field lags behind other branches of linguistics with respect to adopting quantitative methods. Here they provide a theoretically agnostic description of a new framework for quantitatively assessing models and hypotheses in historical linguistics, based on corpus data and using case studies to illustrate how this framework can answer research questions in historical linguistics. The authors offer an in-depth explanation and discussion of the benefits of working with quantitative methods, corpus data, and corpus annotation, and the advantages of open and reproducible research. The book will be a valuable resource for graduate students and researchers in historical linguistics, as well as for all those working with linguistic corpora.
This book investigates the integration of quantitative, corpus-based methodologies into the field of historical linguistics to address long-standing gaps in empirical rigor. Barbara C. McGillivray and Gard B. Jenset, both established researchers in computational and historical linguistics, propose a theoretically agnostic framework designed to test linguistic hypotheses using large-scale data. By advocating for reproducible research practices and systematic corpus annotation, the authors provide a structured approach for scholars to transition from traditional comparative methods to data-driven analysis.
What You Will Find
Experts recognize this work as a foundational text for scholars seeking to modernize historical linguistic inquiry through computational methods. Readers frequently note the technical clarity of the prose, which serves as a bridge between traditional philological approaches and modern data science techniques.
Page Count:
288
Publication Date:
2017-12-05
Publisher:
Oxford University Press
ISBN-10:
0198718179
ISBN-13:
9780198718178
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