In Methods in Latin Computational Linguistics, Barbara McGillivray presents some of the most significant methodological foundations of the emerging field of Latin Computational Linguistics. The reader will find an overview of the computational resources and tools available for Latin and three corpus case studies covering morpho-syntactic and lexical-semantic aspects of Latin verb valency, as well as quantitative diachronic explorations of the argument realization of Latin prefixed verbs. The computational models and the multivariate data analysis techniques employed are explained with a detailed but accessible language. Barbara McGillivray convincingly shows the challenges and opportunities of combining computational methods and historical language data, and contributes to driving the technological change that is affecting Historical Linguistics and the Humanities.

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 presents established and state-of-the-art methods in Language Technology (including text mining, corpus linguistics, computational linguistics, and natural language processing), and demonstrates how they can be applied by humanities scholars working with textual data. The landscape of humanities research has recently changed thanks to the proliferation of big data and large textual collections such as Google Books, Early English Books Online, and Project Gutenberg. These resources have yet to be fully explored by new generations of scholars, and the authors argue that Language Technology has a key role to play in the exploration of large-scale textual data. The authors use a series of illustrative examples from various humanistic disciplines (mainly but not exclusively from History, Classics, and Literary Studies) to demonstrate basic and more complex use-case scenarios. This book will be useful to graduate students and researchers in humanistic disciplines working with textual data, including History, Modern Languages, Literary studies, Classics, and Linguistics. This is also a very useful book for anyone teaching or learning Digital Humanities and interested in the basic concepts from computational linguistics, corpus linguistics, and natural language processing.