Classifying Latin Inscriptions of the Roman Empire: A Machine-Learning Approach

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23330%2F21%3A43962988" target="_blank" >RIV/49777513:23330/21:43962988 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-2989/short_paper12.pdf" target="_blank" >http://ceur-ws.org/Vol-2989/short_paper12.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Classifying Latin Inscriptions of the Roman Empire: A Machine-Learning Approach
Original language description
Large-scale synthetic research in ancient history is often hindered by the incompatibility of tax- onomies used by different digital datasets. Using the example of enriching the Latin Inscriptions from the Roman Empire dataset (LIRE), we demonstrate that machine-learning classification mod- els can bridge the gap between two distinct classification systems and make comparative study possible. We report on training, testing and application of a machine learning classification model using inscription categories from the Epigraphic Database Heidelberg (EDH) to label inscriptions from the Epigraphic Database Claus-Slaby (EDCS). The model is trained on a labeled set of records included in both sources (N=46,171). Several different classification algorithms and parametriza- tions are explored. The final model is based on Extremely Randomized Trees algorithm (ET) and employs 10,055 features, based on several attributes. The final model classifies two thirds of a test dataset with 98% accuracy and 85% of it with 95% accuracy. After model selection and evaluation, we apply the model on inscriptions covered exclusively by EDCS (N=83,482) in an attempt to adopt one consistent system of classification for all records within the LIRE dataset.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the Conference on Computational Humanities Research 2021
ISBN
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ISSN
1613-0073
e-ISSN
1613-0073
Number of pages
13
Pages from-to
123-135
Publisher name
CEUR-WS
Place of publication
Amsterdam
Event location
Amsterdam
Event date
Nov 17, 2021
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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