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Lightweight system extracts person-place relations from historical newspapers

By PulseAugur Editorial · [1 sources] ·

Researchers from DS@GT HIPE have developed a lightweight system for extracting person-place relationships from historical newspapers, focusing on interpretability and efficiency. Their approach utilizes dependency graphs and proximity features, classifying relations with small scikit-learn ensembles or compact Graph Attention Networks, keeping model parameters under 847K. The system achieved a macro recall of 0.5142 on the HIPE-2026 shared task, ranking third for efficiency, and highlighted the importance of minimum character distance for capturing classification signals. The study also emphasized the necessity of document-grouped cross-validation to prevent data leakage. AI

IMPACT This research demonstrates an efficient and interpretable approach to relation extraction, potentially reducing the computational cost for historical text analysis.

RANK_REASON Academic paper detailing a new method for relation extraction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Lightweight system extracts person-place relations from historical newspapers

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Academic paper detailing a new method for relation extraction. [lever_c_demoted from research: ic=1 ai=1.0]
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103 days old
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mlen-Too Wesley ·

    Lightweight Person-Place Relation Extraction from Historical Newspapers with Dependency Graphs and Proximity Features

    arXiv:2607.19718v1 Announce Type: new Abstract: The HIPE-2026 shared task introduces person-place relation extraction from multilingual historical newspapers as a new evaluation track, classifying the at and isAt relations between pre-annotated person and location mentions in Eng…

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