A Ground-Truth Dataset for Article Separation in Historical Newspapers: A ProQuest corpus centered on the China Institute in America (1926-1952)

Overview

This ground-truth dataset contains manually segmented documents, with partial post-OCR correction, derived from an original corpus of news articles focused on the China Institute in America, drawn from the ProQuest collection of Chinese Historical newspapers. The dataset includes 96 articles published between 1926 and 1952.

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
• Title: Article title as provided by ProQuest.
• Source: Periodical in which the article was published (principally China Press, China Weekly Review, North-China Herald) 
• 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 Sino-American cultural institutions
• Media and discourse analysis of Shenbao
• Training/evaluating segmentation and OCR models
• Digital humanities projects requiring high-quality ground truth corpora
• Studies of textual reuse and viral news circulation in Republican-era newspapers