Gakis Panagiotis*, Tsalidis Christos, Stamouli Alexia-Foteini, Zgolompi Ismini, Stamatopoulos Ioannis
University of the Peloponnese, Tripoli 22100, Greece.
*Corresponding author: Gakis Panagiotis
Abstract
The advancement of technology, particularly artificial intelligence, has positively impacted the humanities, strengthening fields such as computational linguistics and natural language processing (NLP). From Winograd’s pioneering SHRDLU system to modern tools like Mnemosyne, significant progress has been made in machine understanding and analysis of language. NLP includes tasks such as information extraction and retrieval, named entity recognition (e.g., persons, locations, events), and the organization of large linguistic datasets. Recognizing these entities is crucial for understanding and organizing information from unstructured texts. Although progress on the Greek language is still limited, it shows promising potential. The General State Archives of Greece (GSA), as the main institution for preserving historical and administrative records in the country, faces challenges due to inconsistent categorization and thematic labeling. Technologies such as lemmatization, normalization, and the use of semantic tools (like SKOS and Mobi) offer solutions to these issues. These tools enable more consistent and hierarchical organization of thematic categories, improving search, navigation, and access for both researchers and the general public. They also support interoperability with national infrastructures such as SearchCulture.gr. The application of ontologies contributes to smarter management of cultural information and ensures unified access to public knowledge.
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How to cite this paper
Information Extraction through Artificial Intelligence in Historical Texts
How to cite this paper: Gakis Panagiotis, Tsalidis Christos, Stamouli Alexia-Foteini, Zgolompi Ismini, Stamatopoulos Ioannis. (2025). Information Extraction through Artificial Intelligence in Historical Texts. Journal of Literature Advances, 2(1), 48-56.
DOI: http://dx.doi.org/10.26855/jla.2025.06.008