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Machine Learning to Read Yesterday's News. How semantic enrichments enhance the study of digitised historical newspapers

Authors: Estelle Bunout; Marten Düring;

Machine Learning to Read Yesterday's News. How semantic enrichments enhance the study of digitised historical newspapers

Abstract

In this workshop we will use the impresso app to explore opportunities and challenges which accompany the semantic enrichment of historical newspapers. We will reflect on the added value of Natural Language Processing techniques such as topic modelling, text reuse detection and word embeddings for historians in conjunction with an introduction and critical assessment of design solutions for the scalable reading of such enriched sources. We target researchers at all (digital) skill levels.

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Keywords

Paper, Forschungsprozess, digitised newspapers, digital source criticism, DHd2024, Webentwicklung, Workshop, NLP, Inhaltsanalyse, Text

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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