frances : cloud-based historical text mining with deep learning and parallel processing
Abstract
frances is an advanced cloud-based text mining digital platform that leverages information extraction, knowledge graphs, natural language processing (NLP), deep learning, and parallel processing techniques. It has been specifically designed to unlock the full potential of historical digital textual collections, such as those from the National Library of Scotland, offering cloud-based capabilities and extended support for complex NLP analyses and data visualizations. frances enables realtime recurrent operational text mining and provides robust capabilities for temporal analysis, accompanied by automatic visualizations for easy result inspection. In this paper, we present the motivation behind the development of frances, emphasizing its innovative design and novel implementation aspects. We also outline future development directions. Additionally, we evaluate the platform through two comprehensive case studies in history and publishing history. Feedback from participants in these studies demonstrates that frances accelerates their work and facilitates rapid testing and dissemination of ideas.
Citation
Yu , L , Charlton , A , Askins , W , Terras , M & Filgueira , R 2023 , frances : cloud-based historical text mining with deep learning and parallel processing . in G A Papadopoulos , R Filgueira & R F Da Silva (eds) , Proceedings : 2023 IEEE 19th international conference on e-science (e-science) . IEEE international conference on e-science , IEEE , Piscataway, NJ , 19th IEEE International Conference on eScience , Limassol , Cyprus , 9/10/23 . https://doi.org/10.1109/e-Science58273.2023.10254798 conference
Publication
Proceedings
ISSN
2325-372XType
Conference item
Collections
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