mlsanityData to 14 Oct 2026

Contrastive Entity Coreference and Disambiguation for Historical Texts

arXiv 2406.15576 ↗First posted 21 Jun 2024cs.CL econ.GN q-fin.ECcited by 3

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Abstract (arXiv)

Massive-scale historical document collections are crucial for social science research. Despite increasing digitization, these documents typically lack unique cross-document identifiers for individuals mentioned within the texts, as well as individual identifiers from external knowledgebases like Wikipedia/Wikidata. Existing entity disambiguation methods often fall short in accuracy for historical documents, which are replete with individuals not remembered in contemporary knowledgebases. This study makes three key contributions to improve cross-document coreference resolution and disambiguation in historical texts: a massive-scale training dataset replete with hard negatives - that sources over 190 million entity pairs from Wikipedia contexts and disambiguation pages - high-quality evaluation data from hand-labeled historical newswire articles, and trained models evaluated on this historical benchmark. We contrastively train bi-encoder models for coreferencing and disambiguating individuals in historical texts, achieving accurate, scalable performance that identifies out-of-knowledgebase individuals. Our approach significantly surpasses other entity disambiguation models on our historical newswire benchmark. Our models also demonstrate competitive performance on modern entity disambiguation benchmarks, particularly certain news disambiguation datasets.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 18 references that are in the map. First posted on arXiv 21 Jun 2024.

Builds on

13 of 18 references.

References · 13

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1 more
  1. 2024Newswire: A Large-Scale Structured Database of a Century of Historical Newscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Mah=an=ama: A Unique Testbed for Literary Entity Discovery and Linkingcited by 0
  2. 2026Small Language Model Helps Resolve Semantic Ambiguity of LLM Promptcited by 0
  3. 2026Beyond Scale and Generation: Understanding Language Model-based Entity Matchingcited by 0