![[2602.17663] CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.17663] CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts
Summary
The paper presents the HIPE-2026 evaluation lab focused on extracting person-place relations from multilingual historical texts, enhancing previous HIPE campaigns.
Why It Matters
This research is significant as it addresses the challenges of extracting meaningful relations from noisy historical data, which is crucial for applications in digital humanities, knowledge graph construction, and historical analysis. By improving accuracy and efficiency in this domain, it supports better understanding and utilization of historical texts.
Key Takeaways
- HIPE-2026 focuses on person-place relation extraction from multilingual historical texts.
- The evaluation lab introduces a three-fold assessment of accuracy, efficiency, and generalization.
- It builds on previous HIPE campaigns, enhancing methods for semantic relation extraction.
- The research supports applications in knowledge graph construction and historical biography.
- Temporal and geographical reasoning is essential for classifying person-place relations.
Computer Science > Artificial Intelligence arXiv:2602.17663 (cs) [Submitted on 19 Feb 2026] Title:CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts Authors:Juri Opitz, Corina Raclé, Emanuela Boros, Andrianos Michail, Matteo Romanello, Maud Ehrmann, Simon Clematide View a PDF of the paper titled CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts, by Juri Opitz and 6 other authors View PDF HTML (experimental) Abstract:HIPE-2026 is a CLEF evaluation lab dedicated to person-place relation extraction from noisy, multilingual historical texts. Building on the HIPE-2020 and HIPE-2022 campaigns, it extends the series toward semantic relation extraction by targeting the task of identifying person--place associations in multiple languages and time periods. Systems are asked to classify relations of two types - $at$ ("Has the person ever been at this place?") and $isAt$ ("Is the person located at this place around publication time?") - requiring reasoning over temporal and geographical cues. The lab introduces a three-fold evaluation profile that jointly assesses accuracy, computational efficiency, and domain generalization. By linking relation extraction to large-scale historical data processing, HIPE-2026 aims to support downstream applications in knowledge-graph construction, historical biography reconstruction, and spatial analysis in digital huma...
