Language and chronology : text dating by machine learning / / Gregory Toner, 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 met...
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| Superior document: | Language and Computers; volume84 |
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| TeilnehmendeR: | |
| Place / Publishing House: | Leiden Boston : : Brill | Rodopi,, 2019. |
| Year of Publication: | 2019 |
| Language: | English |
| Series: | Language and Computers;
volume84. |
| Physical Description: | 1 online resource (xii, 183 pages) :; illustrations. |
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| Other title: | Front Matter -- Copyright Page / |
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| Summary: | 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. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 900441004X |
| Hierarchical level: | Monograph |
| Statement of Responsibility: | Gregory Toner, Xiwu Han. |