{"id":1173,"date":"2024-12-24T10:49:55","date_gmt":"2024-12-24T09:49:55","guid":{"rendered":"https:\/\/www.cjvt.si\/llm4dh\/?page_id=1173"},"modified":"2025-05-14T12:29:24","modified_gmt":"2025-05-14T10:29:24","slug":"challenge-4","status":"publish","type":"page","link":"https:\/\/www.cjvt.si\/llm4dh\/en\/work-packages\/work-package-4\/","title":{"rendered":"Challenge 4: Advanced Technologies for Digital Humanities"},"content":{"rendered":"

Challenge 4: Advanced Technologies for Digital Humanities <\/strong><\/h1>\n<\/div><\/section><\/div>\n

Specific important tasks in DH require novel advanced language technologies or adaptations of existing general approaches. In this challenge, we present four technologies, solving general problems, which we adapt to specific DH tasks in the next challenge.<\/p>\n<\/div><\/section><\/div>\n<\/div><\/div><\/div><\/div><\/div>

Task 4.1<\/span><\/span><\/span><\/span><\/a>Task 4.2<\/span><\/span><\/span><\/span><\/a>Task 4.3<\/span><\/span><\/span><\/span><\/a>Task 4.4<\/span><\/span><\/span><\/span><\/a>Yearly reports<\/span><\/span><\/span><\/span><\/a><\/div>
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T4.1 Named entity graphs <\/em><\/strong><\/h3>\n<\/div><\/section>
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There is a notable need to investigate networks of people, organizations, places, and other entities across various corpora. Current Named Entity Recognition (NER) and Linking (NEL) methods (Boros et al., 2020) are not adequately adapted for less-resource languages, leaving these languages underrepresented in multilingual historical corpora (Schweter et al., 2022). This inadaptability limits the ability to build comprehensive interaction graphs for analyzing important entities and their connections across time periods. While complex network analysis can help rank and visualize these entities, utilizing pretrained, language-agnostic models like ULTRA (Galkin et al., 2024) for reasoning over these graphs and extracting meaningful, language-agnostic data can further enhance the analysis of historical and contemporary newspapers but also parliamentary transcripts and literature. By constructing and visualizing comprehensive interaction graphs, we aim to significantly improve our understanding of relationships, sentiment, and narrative structures within these texts, providing a much-needed resource for Slovenian and other less-resource languages.<\/p>\n<\/div>\n

To investigate networks of people, organizations, places, and other entities across various corpora, we will adapt NER and NEL methods, such as (Prelevikj and \u017ditnik, 2021), for Slovenian and multilingual historical corpora. This will enable the construction of interaction graphs for analyzing significant entities and their connections in both contemporary and historical contexts. Complex network analysis will rank and visualize key entities and motifs in the networks. We will use pretrained, language-agnostic models like ULTRA to reason over the graphs.<\/p>\n

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