Language and chronology : text dating by machine learning
著者
書誌事項
Language and chronology : text dating by machine learning
（Language and computers : studies in practical linguistics, v. 84）
Brill, c2019
- : hardback
大学図書館所蔵 全6件
注記
Includes bibliographical references (p. [165]-180) and index
内容説明・目次
内容説明
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.
目次
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
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