Lightweight Person-Place Relation Extraction from Historical Newspapers with Dependency Graphs and Proximity Features
Summary
A lightweight, interpretable system was developed for person-place relation extraction from multilingual historical newspapers, specifically for the HIPE-2026 shared task. This approach, submitted by team DS@GT HIPE, avoids pretrained language models at the relation classification stage, instead building a document-level graph from dependency parses and extracting proximity-based and part-of-speech features. These features are then classified using small scikit-learn ensembles or compact Graph Attention Networks, keeping all submitted runs under 847K parameters. On the official Test A evaluation, the best run achieved a macro recall of 0.5142, ranking 3rd on the Efficiency profile among 17 participating teams. Key findings indicate that minimum character distance alone captures most classification signal, and document-grouped cross-validation is essential to prevent score inflation by 25-37 percentage points due to recurring entity mentions.
Key takeaway
For NLP engineers building relation extraction systems, especially with historical or large-scale textual archives, your feature engineering efforts should prioritize simple proximity measures like minimum character distance. This approach offers high efficiency and interpretability. Crucially, ensure your evaluation methodology employs document-grouped cross-validation to avoid significantly inflated performance metrics caused by data leakage from recurring entity mentions across documents.
Key insights
Minimum character distance is a surprisingly strong signal for person-place relation extraction in historical texts.
Principles
- Argument distance often dominates relation extraction signals.
- Document-grouped cross-validation prevents data leakage with recurring entities.
Method
Build a document-level graph from dependency parses, extract proximity and part-of-speech features, then classify with small scikit-learn ensembles or compact Graph Attention Networks.
In practice
- Prioritize minimum character distance as a primary feature.
- Implement document-grouped cross-validation for historical text corpora.
Topics
- Person-Place Relation Extraction
- Historical Newspapers
- Dependency Parsing
- Feature Engineering
- Graph Attention Networks
- Cross-Validation
- HIPE-2026
Best for: Research Scientist, AI Scientist, NLP Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.