ZBÍRAL, David, Adam MERTEL and Robert Laurence John SHAW. Maximising the Power of Semantic Textual Data : CASTEMO Data Collection and the InkVisitor Application. In Computing the Past : Computational Approaches to the Dynamics of Cultures and Societies, 6-8 October 2022, Plzeň, Czech Republic. 2022.
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Basic information
Original name Maximising the Power of Semantic Textual Data : CASTEMO Data Collection and the InkVisitor Application
Authors ZBÍRAL, David (203 Czech Republic, guarantor, belonging to the institution), Adam MERTEL (703 Slovakia, belonging to the institution) and Robert Laurence John SHAW (826 United Kingdom of Great Britain and Northern Ireland, belonging to the institution).
Edition Computing the Past : Computational Approaches to the Dynamics of Cultures and Societies, 6-8 October 2022, Plzeň, Czech Republic, 2022.
Other information
Original language English
Type of outcome Presentations at conferences
Field of Study 60304 Religious studies
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/00216224:14210/22:00126923
Organization unit Faculty of Arts
Keywords (in Czech) sémantické modelování textu; digital humanities
Keywords in English semantic text modelling; digital humanities
Tags rivok
Tags International impact, Reviewed
Changed by Changed by: Mgr. Ivona Vrzalová, učo 361753. Changed: 22/3/2023 13:54.
Abstract
In this paper, we present Computer-Assisted Semantic Text Modelling (CASTEMO), a novel approach to transformation of textual resources into deeply structured data stored in JSON-based document databases. We also present the InkVisitor application which assists this data collection workflow and helps validate the data. Both the workflow and the application were developed within the Dissident Networks Project (DISSINET, https://dissinet.cz).
Links
101000442, interní kód MUName: Networks of Dissent: Computational Modelling of Dissident and Inquisitorial Cultures in Medieval Europe (Acronym: DISSINET)
Investor: European Union, ERC (Excellent Science)
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