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This paper explores lightweight methods for person-place relation extraction from multilingual historical newspapers, focusing on the classification of "at" and "isAt" relations without relying on pretrained language models. By constructing document-level graphs from dependency parses and utilizing proximity-based and part-of-speech features, the authors achieved a macro recall of 0.5142, ranking 3rd in efficiency among 17 teams. The study highlights that minimum character distance is a significant predictor for classification, while emphasizing the necessity of document-grouped cross-validation to mitigate data leakage effects in performance evaluation.
Minimum character distance alone captures most of the classification signal, challenging the assumption that more complex features are always beneficial.
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 English, French, and German. Motivated by the cost of processing historical archives at scale, our team (DS@GT HIPE, team 2 in the official results) investigates how far a lightweight, interpretable system can go without any pretrained language model at the relation classification stage. Our approach builds a document-level graph from dependency parses, extracts proximity-based and part-of-speech features for each entity pair, and classifies them with small scikit-learn ensembles or compact Graph Attention Networks, keeping every submitted run under 847K parameters. On the official evaluation (Test A, the newspaper test set), our best run reached a macro recall of 0.5142, ranking 3rd on the Efficiency profile while placing mid-table on Accuracy among the 17 participating teams. Two findings stand out. First, minimum character distance alone captures most of the classification signal; adding further engineered features yields inconsistent gains and sometimes degrades performance, echoing prior evidence that argument distance dominates relation extraction. Second, document-grouped cross-validation is essential on this corpus: pair-level splits inflate scores by 25-37 percentage points because entity mentions recur across documents, a data-leakage effect that grouped cross-validation removes.