frances: cloud-based historical text mining with deep learning and parallel processing

Lilin Yu; Ash Charlton; Wilfrid  Askins; Melissa Terras; Rosa Filgueira

doi:10.1109/e-Science58273.2023.10254798

frances: cloud-based historical text mining with deep learning and parallel processing

Lilin Yu^*, Ash Charlton, Wilfrid Askins, Melissa Terras, Rosa Filgueira^*

^*Corresponding author for this work

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	2023 IEEE 19th international conference on e-science (e-science)
Editors	George Angelos Papadopoulos, Rosa Filgueira, Rafael Ferreira Da Silva
Place of Publication	Piscataway, NJ
Publisher	IEEE
Chapter	10254798
Number of pages	10
ISBN (Electronic)	9798350322231
ISBN (Print)	9798350322248
DOIs	https://doi.org/10.1109/e-Science58273.2023.10254798
Publication status	Published - 25 Sept 2023
Event	19th IEEE International Conference on eScience - Limassol, Cyprus, Limassol, Cyprus Duration: 9 Oct 2023 → 13 Oct 2023 Conference number: 19 https://www.escience-conference.org/2023/

Publication series

Name	IEEE international conference on e-science
ISSN (Print)	2325-372X
ISSN (Electronic)	2325-3703

Conference

Conference	19th IEEE International Conference on eScience
Abbreviated title	eScience
Country/Territory	Cyprus
City	Limassol
Period	9/10/23 → 13/10/23
Internet address	https://www.escience-conference.org/2023/

Keywords

Digitised historical collections
Information extraction
Apache Spark
Parallel processing
Text mining
Cloud-based platform
Knowledge graphs
Natural language processing

Access to Document

10.1109/e-Science58273.2023.10254798

frances_submitted
Copyright © 2023 IEEE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/e-Science58273.2023.10254798.
Accepted author manuscript, 3.49 MB

Cite this

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. https://doi.org/10.1109/e-Science58273.2023.10254798

Yu, Lilin ; Charlton, Ash ; Askins, Wilfrid et al. / frances : cloud-based historical text mining with deep learning and parallel processing. Proceedings: 2023 IEEE 19th international conference on e-science (e-science). editor / George Angelos Papadopoulos ; Rosa Filgueira ; Rafael Ferreira Da Silva. Piscataway, NJ : IEEE, 2023. (IEEE international conference on e-science).

@inproceedings{ff68aa5120f44e62ac65de6c1b8df20a,

title = "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 testingand dissemination of ideas.",

keywords = "Digitised historical collections, Information extraction, Apache Spark, Parallel processing, Text mining, Cloud-based platform, Knowledge graphs, Natural language processing",

author = "Lilin Yu and Ash Charlton and Wilfrid Askins and Melissa Terras and Rosa Filgueira",

year = "2023",

month = sep,

day = "25",

doi = "10.1109/e-Science58273.2023.10254798",

language = "English",

isbn = "9798350322248",

series = "IEEE international conference on e-science",

publisher = "IEEE",

editor = "Papadopoulos, {George Angelos} and Rosa Filgueira and {Da Silva}, {Rafael Ferreira}",

booktitle = "Proceedings",

note = "19th IEEE International Conference on eScience, eScience ; Conference date: 09-10-2023 Through 13-10-2023",

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Yu, L, Charlton, A, Askins, W, Terras, M & Filgueira, R 2023, frances: cloud-based historical text mining with deep learning and parallel processing. in GA Papadopoulos, R Filgueira & RF 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

frances: cloud-based historical text mining with deep learning and parallel processing. / Yu, Lilin; Charlton, Ash; Askins, Wilfrid et al.
Proceedings: 2023 IEEE 19th international conference on e-science (e-science). ed. / George Angelos Papadopoulos; Rosa Filgueira; Rafael Ferreira Da Silva. Piscataway, NJ: IEEE, 2023. (IEEE international conference on e-science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - frances

T2 - 19th IEEE International Conference on eScience

AU - Yu, Lilin

AU - Charlton, Ash

AU - Askins, Wilfrid

AU - Terras, Melissa

AU - Filgueira, Rosa

N1 - Conference code: 19

PY - 2023/9/25

Y1 - 2023/9/25

N2 - 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 testingand dissemination of ideas.

AB - 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 testingand dissemination of ideas.

KW - Digitised historical collections

KW - Information extraction

KW - Apache Spark

KW - Parallel processing

KW - Text mining

KW - Cloud-based platform

KW - Knowledge graphs

KW - Natural language processing

UR - https://doi.org/10.1109/e-Science58273.2023

U2 - 10.1109/e-Science58273.2023.10254798

DO - 10.1109/e-Science58273.2023.10254798

M3 - Conference contribution

SN - 9798350322248

T3 - IEEE international conference on e-science

BT - Proceedings

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A2 - Filgueira, Rosa

A2 - Da Silva, Rafael Ferreira

PB - IEEE

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ER -

Yu L, Charlton A, Askins W, Terras M, Filgueira R. frances: cloud-based historical text mining with deep learning and parallel processing. In Papadopoulos GA, Filgueira R, Da Silva RF, editors, Proceedings: 2023 IEEE 19th international conference on e-science (e-science). Piscataway, NJ: IEEE. 2023. (IEEE international conference on e-science). doi: 10.1109/e-Science58273.2023.10254798

frances: cloud-based historical text mining with deep learning and parallel processing

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