Classifying Latin Inscriptions of the Roman Empire: A Machine-Learning Approach
| dc.contributor.author | Ehrmann, Maud | |
| dc.contributor.author | Kaše, Vojtěch | |
| dc.contributor.author | Karsdorp, Folgert | |
| dc.contributor.author | Heřmánková, Petra | |
| dc.contributor.author | Wevers, Melvin | |
| dc.contributor.author | Sobotková, Adéla | |
| dc.contributor.author | Andrews, Tara Lee | |
| dc.contributor.author | Burghardt, Manuel | |
| dc.contributor.author | Kestemont, Mike | |
| dc.contributor.author | Manjavacas, Enrique | |
| dc.contributor.author | Piotrowski, Michael | |
| dc.contributor.author | van Zundert, Joris | |
| dc.date.accessioned | 2022-02-14T11:00:15Z | |
| dc.date.available | 2022-02-14T11:00:15Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract-translated | 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. | en |
| dc.format | 13 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | KAŠE, V. HEŘMÁNKOVÁ, P. SOBOTKOVÁ, A. Classifying Latin Inscriptions of the Roman Empire: A Machine-Learning Approach. In Ehrmann, M., Karsdorp, F., Wevers, M. Proceedings of the Conference on Computational Humanities Research 2021. Amsterdam: CEUR-WS, 2021. s. 123-135. ISBN: neuvedeno , ISSN: 1613-0073 | cs |
| dc.identifier.isbn | neuvedeno | |
| dc.identifier.issn | 1613-0073 | |
| dc.identifier.obd | 43933987 | |
| dc.identifier.uri | http://hdl.handle.net/11025/46904 | |
| dc.language.iso | en | en |
| dc.publisher | CEUR-WS | en |
| dc.relation.ispartofseries | Proceedings of the Conference on Computational Humanities Research 2021 | en |
| dc.rights | © authors | en |
| dc.rights.access | openAccess | en |
| dc.subject.translated | Latin inscriptions | en |
| dc.subject.translated | document classification | en |
| dc.subject.translated | comparative analysis | en |
| dc.subject.translated | Roman Empire | en |
| dc.title | Classifying Latin Inscriptions of the Roman Empire: A Machine-Learning Approach | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | ConferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |