Exploring archives with probabilistic models: Topic Modelling for the valorisation of digitised archives of the European Commission

Simon Hengchen, Mathias Coeckelbergs, Seth van Hooland, Ruben Verborgh, Thomas Steiner

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

Topic Modelling (TM) has gained momentum over the last few years within the humanities to analyze topics represented in large volumes of full text. This paper proposes an experiment with the usage of TM based on a large subset of digitized archival holdings of the European Commission (EC). Currently, millions of scanned and OCRed files are available and hold the potential to significantly change the way historians of the construction and evolution of the European Union can perform their research. However, due to a lack of resources, only minimal metadata are available on a file and document level, seriously undermining the accessibility of this archival collection. The article explores in an empirical manner the possibilities and limits of TM to automatically extract key concepts from a large body of documents spanning multiple decades. By mapping the topics to headings of the EUROVOC thesaurus, the proof of concept described in this paper offers the future possibility to represent the identified topics with the help of a hierarchical search interface for end-users.
Originalspråkengelska
Titel på gästpublikation2016 IEEE International Conference on Big Data
UtgivningsortWashington, DC, USA
FörlagIEEE
Utgivningsdatum5 dec 2016
ISBN (tryckt)978-1-4673-9006-4
ISBN (elektroniskt)978-1-4673-9005-7
StatusPublicerad - 5 dec 2016
MoE-publikationstypA4 Artikel i en konferenspublikation

Vetenskapsgrenar

  • 6121 Språkvetenskaper
  • 113 Data- och informationsvetenskap

Citera det här

Hengchen, S., Coeckelbergs, M., van Hooland, S., Verborgh, R., & Steiner, T. (2016). Exploring archives with probabilistic models: Topic Modelling for the valorisation of digitised archives of the European Commission. I 2016 IEEE International Conference on Big Data Washington, DC, USA: IEEE.
Hengchen, Simon ; Coeckelbergs, Mathias ; van Hooland, Seth ; Verborgh, Ruben ; Steiner, Thomas. / Exploring archives with probabilistic models: Topic Modelling for the valorisation of digitised archives of the European Commission. 2016 IEEE International Conference on Big Data. Washington, DC, USA : IEEE, 2016.
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abstract = "Topic Modelling (TM) has gained momentum over the last few years within the humanities to analyze topics represented in large volumes of full text. This paper proposes an experiment with the usage of TM based on a large subset of digitized archival holdings of the European Commission (EC). Currently, millions of scanned and OCRed files are available and hold the potential to significantly change the way historians of the construction and evolution of the European Union can perform their research. However, due to a lack of resources, only minimal metadata are available on a file and document level, seriously undermining the accessibility of this archival collection. The article explores in an empirical manner the possibilities and limits of TM to automatically extract key concepts from a large body of documents spanning multiple decades. By mapping the topics to headings of the EUROVOC thesaurus, the proof of concept described in this paper offers the future possibility to represent the identified topics with the help of a hierarchical search interface for end-users.",
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Hengchen, S, Coeckelbergs, M, van Hooland, S, Verborgh, R & Steiner, T 2016, Exploring archives with probabilistic models: Topic Modelling for the valorisation of digitised archives of the European Commission. i 2016 IEEE International Conference on Big Data. IEEE, Washington, DC, USA.

Exploring archives with probabilistic models: Topic Modelling for the valorisation of digitised archives of the European Commission. / Hengchen, Simon; Coeckelbergs, Mathias; van Hooland, Seth; Verborgh, Ruben; Steiner, Thomas.

2016 IEEE International Conference on Big Data. Washington, DC, USA : IEEE, 2016.

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

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AU - Coeckelbergs, Mathias

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AB - Topic Modelling (TM) has gained momentum over the last few years within the humanities to analyze topics represented in large volumes of full text. This paper proposes an experiment with the usage of TM based on a large subset of digitized archival holdings of the European Commission (EC). Currently, millions of scanned and OCRed files are available and hold the potential to significantly change the way historians of the construction and evolution of the European Union can perform their research. However, due to a lack of resources, only minimal metadata are available on a file and document level, seriously undermining the accessibility of this archival collection. The article explores in an empirical manner the possibilities and limits of TM to automatically extract key concepts from a large body of documents spanning multiple decades. By mapping the topics to headings of the EUROVOC thesaurus, the proof of concept described in this paper offers the future possibility to represent the identified topics with the help of a hierarchical search interface for end-users.

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Hengchen S, Coeckelbergs M, van Hooland S, Verborgh R, Steiner T. Exploring archives with probabilistic models: Topic Modelling for the valorisation of digitised archives of the European Commission. I 2016 IEEE International Conference on Big Data. Washington, DC, USA: IEEE. 2016