Authors:
Lorella Viola
1
and
Jaap Verheul
2
Affiliations:
1
Luxembourg Centre for Contemporary and Digital History (C2DH), University of Luxembourg, Belval Campus, Maison des Sciences Humaines, 11, Porte des Sciences, L-4366 Esch-sur-Alzette, Luxembourg
;
2
Department of History and Art History, Utrecht University, Drift 6, 3512 BS, Utrecht, The Netherlands
Keyword(s):
Machine Learning, Sequence Tagging, Spatial Humanities, Geographical Enrichment, Immigrant Newspapers.
Abstract:
This paper discusses the added value of applying machine learning (ML) to contextually enrich digital collections. In this study, we employed ML as a method to geographically enrich historical datasets. Specifically, we used a sequence tagging tool (Riedl and Padó 2018) which implements TensorFlow to perform NER on a corpus of historical immigrant newspapers. Afterwards, the entities were extracted and geocoded. The aim was to prepare large quantities of unstructured data for a conceptual historical analysis of geographical references. The intention was to develop a method that would assist researchers working in spatial humanities, a recently emerged interdisciplinary field focused on geographic and conceptual space. Here we describe the ML methodology and the geocoding phase of the project, focussing on the advantages and challenges of this approach, particularly for humanities scholars. We also argue that, by choosing to use largely neglected sources such as immigrant newspapers (
also known as ethnic newspapers), this study contributes to the debate about diversity representation and archival biases in digital practices.
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