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Datasets:
oberbics
/
Topic-specific-genre-classification_german_historical-newspapers

Tasks:
Text Classification
Modalities:
Image
Text
Formats:
csv
Languages:
German
Size:
< 1K
Tags:
history
newspapers
emigration
DOI:
Libraries:
Datasets
pandas
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metadata
task_categories:
  - text-classification
language:
  - de
tags:
  - history
  - newspapers
  - emigration
pretty_name: Topic-specific Genre Classification of German Historical Newspapers
size_categories:
  - n<1K
license: afl-3.0

Dataset Card for Topic-specific Genre Classification of German Historical Newspapers

This dataset was developed to evaluate topic-specific genre classification of German-language historical newspaper clippings. The dataset - which can be split into training and texting - was compiled using keyword searches for various German terms related to emigration, including "Auswander" (emigrant), "Ausgewanderte" (emigrated), "Emigrant" (emigrant), "Emigrierte" (emigrated), "Emigration" (emigration), "Kolonist" (colonist), and "Ansiedler" (settler). The clippings were then enriched with human annotations categorizing them into different genres: news, advertisements, culture, information, finances, statics, and criminality articles.

  • Curated by: [Sarah Oberbichler]
  • Language(s) (NLP): [German]
  • License: [afl-3.0]

Dataset Sources [optional]

Uses

Evaluation of genre-classification of historical newspapers on the topic of emigration

Dataset Structure

The dataset consists of digitized historical German newspaper articles related to emigration, with each entry containing several structured fields. Each record includes a unique document identifier that combines the newspaper name, publication date, and article number. The temporal information is provided through precise dating (date and time), and each article is linked to its digital representation via a IIIF (International Image Interoperability Framework) of the NewsEye platform: https://platform2.newseye.eu/users/sign_in

Dataset Creation

Curation Rationale

Evaluation of ML models in topic-specific classification of OCR'd text of different quality

Source Data

  • Arbeiter Zeitung
  • Illustrierte Kronen Zeitung
  • Neue Freie Presse
  • Innsbrucker Nachrichten

Who are the source data producers?

Austrian National Library

Annotations

Human annotations

Annotation process

Manual annotation

Who are the annotators?

Sarah Oberbichler

Personal and Sensitive Information

Bias, Risks, and Limitations

Recommendations

Citation

Dataset Card Authors [optional]

Sarah Oberbichler

Dataset Card Contact

Sarah Oberbichler ([email protected])