Language and Chronology

Text dating by machine learning

Series:

Editors: Gregory Toner and Xiwu Han
In Language and Chronology, Toner and Han apply innovative Machine Learning techniques to the problem of the dating of literary texts. Many ancient and medieval literatures lack reliable chronologies which could aid scholars in locating texts in their historical context. The new machine-learning method presented here uses chronological information gleaned from annalistic records to date a wide range of texts. The method is also applied to multi-layered texts to aid the identification of different chronological strata within single copies.
While the algorithm is here applied to medieval Irish material of the period c.700-c.1700, it can be extended to written texts in any language or alphabet. The authors’ approach presents a step change in Digital Humanities, moving us beyond simple querying of electronic texts towards the production of a sophisticated tool for literary and historical studies.

E-Book List price

EUR €88.00USD $106.00

Biographical Note

Gregory Toner is Professor of Irish at Queen’s University, Belfast. He has published widely on medieval Irish literature and language and is editor of the electronic Dictionary of the Irish Language.

Xiwu Han, Ph.D. (2006), Harbin Institute of Technology, China, has published numerous peer-reviewed articles on computational linguistics and machine translation.

Table of contents

Contents List of Illustrations Abbreviations Introduction 0.1 Automated Dating Methods 0.1 How to Read This Book 1 Dating Texts: Principles and Methods 1.1 Introduction 1.2 Texts by Known Authors 1.3 Internal Evidence 1.4 Manuscripts 1.5 Intertextuality 1.6 Metrics 1.7 Linguistic Dating 1.8 Conclusion 2 Computational Approaches to Text Dating 2.1 A Brief History 2.2 The Problem Stated 2.3 Previous Solutions 2.4 New Solutions 2.5 Datability 2.6 Conclusion 3 Trials in English and Medieval Irish Texts 3.1 Dating English Texts 3.2 Dating Medieval Irish Texts 3.3 Implementation 3.4 Temporal Parameters 3.5 Datability 3.6 Conclusion 4 Dating Long Documents 4.0 Introduction 4.1 Building a Datable Medieval Irish Corpus 4.2 Dating Long Documents 4.3 Establishing the Date of Composition 4.4 Transmission and Manuscript Dates 4.5 Focussed Dating Predictions 4.6 Periodization 4.7 Stratification 4.8 Conclusion Conclusion 5.1 A Temporal Model 5.2 Towards a Tool: Computational Chronometrics 5.3 Applicability to Other Literatures Appendix A: Conventional Dating of Texts Used in This Study A.1 Texts Appendix B: Machine Learning B.1 Classification, Regression and Clustering B.2 Other Relevant Statistics Bibliography Index

Readership

Scholars concerned with dating ancient and medieval texts, e.g. in Biblical Studies, Anglo-Saxon and Celtic. Also, Digital Humanities scholars interested in high-level applications of machine learning for solving complex problems.

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