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

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

60102 - Archaeology

Projekt

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Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Conference on Computational Humanities Research 2021

ISBN

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ISSN

1613-0073

e-ISSN

1613-0073

Počet stran výsledku

13

Strana od-do

123-135

Název nakladatele

CEUR-WS

Místo vydání

Amsterdam

Místo konání akce

Amsterdam

Datum konání akce

17. 11. 2021

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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