Language and chronology : text dating by machine learning
Author(s)
Bibliographic Information
Language and chronology : text dating by machine learning
(Language and computers : studies in practical linguistics, v. 84)
Brill, c2019
- : hardback
Available at 6 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references (p. [165]-180) and index
Description and Table of Contents
Description
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.
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
by "Nielsen BookData"