Machine Learning to Geographically Enrich Understudied Sources: A Conceptual Approach

Lorella Viola, Jaap Verheul

2020

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.

Paper Citation

in Harvard Style

Viola L. and Verheul J. (2020). Machine Learning to Geographically Enrich Understudied Sources: A Conceptual Approach. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ARTIDIGH, ISBN 978-989-758-395-7, pages 469-475. DOI: 10.5220/0009094204690475

in Bibtex Style

@conference{artidigh20,
author={Lorella Viola and Jaap Verheul},
title={Machine Learning to Geographically Enrich Understudied Sources: A Conceptual Approach},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ARTIDIGH,},
year={2020},
pages={469-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009094204690475},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ARTIDIGH,
TI - Machine Learning to Geographically Enrich Understudied Sources: A Conceptual Approach
SN - 978-989-758-395-7
AU - Viola L.
AU - Verheul J.
PY - 2020
SP - 469
EP - 475
DO - 10.5220/0009094204690475