Summary
HistText is a groundbreaking application developed to address the complex challenges of large-scale data mining in textual corpora, with a particular focus on historical documents. Created in the context of the ERC-funded ENP-China project, which aims to study the evolution of Chinese elites from the 19th century to 1949, HistText is the result of a synergistic collaboration between historians and computer scientists exploring machine learning applications for extensive text archives. Designed to manage databases containing billions of words across millions of multilingual documents, HistText offers a robust and versatile platform that streamlines the process of extracting and visualizing valuable insights. The application features a user-friendly interface, advanced text analysis techniques, and powerful data visualization capabilities. It provides a simplified approach for novice users to conduct complex data queries and analyses, while also offering a comprehensive R-library for more expert users. The main challenge that the proof of concept aims to tackle is to make HistText a fully packageable and transferable tool that can cater to the specialized needs of scholars and institutions holding vast digital repositories. With its focus on advanced text analysis and user accessibility, HistText stands as an invaluable resource not only for academics in the digital humanities but also for students and the general public. In terms of broader applications, HistText has the potential to be integrated into a wide range of institutions (libraries, digital content providers, etc.). The platform is exceptionally well-suited for analyzing a wide range of text genres, including newspapers, periodicals, directories, and diaries, among others. By offering a scalable, user-friendly, and methodologically rigorous tool, HistText aims to revolutionize how we approach large-scale textual analysis, providing a new pathway for understanding historical documents.
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| Web resources: | https://cordis.europa.eu/project/id/101188025 |
| Start date: | 01-09-2024 |
| End date: | 28-02-2026 |
| Total budget - Public funding: | - 150 000,00 Euro |
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Original description
HistText is a groundbreaking application developed to address the complex challenges of large-scale data mining in textual corpora, with a particular focus on historical documents. Created in the context of the ERC-funded ENP-China project, which aims to study the evolution of Chinese elites from the 19th century to 1949, HistText is the result of a synergistic collaboration between historians and computer scientists exploring machine learning applications for extensive text archives. Designed to manage databases containing billions of words across millions of multilingual documents, HistText offers a robust and versatile platform that streamlines the process of extracting and visualizing valuable insights. The application features a user-friendly interface, advanced text analysis techniques, and powerful data visualization capabilities. It provides a simplified approach for novice users to conduct complex data queries and analyses, while also offering a comprehensive R-library for more expert users. The main challenge that the proof of concept aims to tackle is to make HistText a fully packageable and transferable tool that can cater to the specialized needs of scholars and institutions holding vast digital repositories. With its focus on advanced text analysis and user accessibility, HistText stands as an invaluable resource not only for academics in the digital humanities but also for students and the general public. In terms of broader applications, HistText has the potential to be integrated into a wide range of institutions (libraries, digital content providers, etc.). The platform is exceptionally well-suited for analyzing a wide range of text genres, including newspapers, periodicals, directories, and diaries, among others. By offering a scalable, user-friendly, and methodologically rigorous tool, HistText aims to revolutionize how we approach large-scale textual analysis, providing a new pathway for understanding historical documents.Status
SIGNEDCall topic
ERC-2024-POCUpdate Date
23-11-2024
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