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Ruben Verborgh

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

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

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 OCR’ed 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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Published in 2016 in Proceedings of the First Workshop on Computational Archival Science.

Keywords: metadata, research

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