A Ground-Truth Dataset for Article Separation in Historical Newspapers: A Shenbao corpus centered on 中華教育文化基金會 (1924–1947)

This ground-truth dataset contains carefully curated and properly segmented documents derived from an original corpus of news articles focused on China Foundation for the Promotion of Education and Culture 中華教育文化基金會 (established in 1924 to manage the Second remission of the U.S. Boxer Indemnity. The dataset includes 379 articles published in the Shenbao 申報 newspaper between 1924 and 1947.

The ground-truth data contains the following fields:

• DocId: Unique identifier as stored  in the Modern China Textual Database (MCTB).
• Date: Original date of publication
• Section, section2: Newspaper section in which the article appeared (automatically retrieved from the Title or full text)
• Title: Title as provided by the data supplier
• Source: Shenbao
• Text: Original, unsegmented text
• text_seg: Historian-curated segmented text, produced using GPT + close reading
• length: Character/word length of the original text
• length_seg: Character/word length after re-segmentation
• diff: length difference between original and segmented text

The segmentation process uses a hybrid human–AI workflow: an automated step with a GPT-based “Historical Text Segmenter,” followed by detailed historian-guided verification and correction. The result is a high-quality ground-truth dataset suitable for OCR benchmarking, segmentation modeling, historical text analysis, and digital humanities research. Additional documentation on the configuration of the GPT “Historical Text Segmenter” is available here.

Use Cases

This dataset is intended for:

• Historical research on academic institutions and Sino-American cultural relations
• Media and discourse analysis of Shenbao
• Training/evaluating segmentation models
• Digital humanities projects requiring high-quality ground truth corpora
• Studies of textual reuse and viral news circulation in Republican-era newspapers