CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts

This is the accepted manuscript (post-print) of the paper published in ECIR 2026. Lecture Notes in Computer Science, vol 16486. Springer, Cham. The final authenticated version is available at Springer via DOI: https://doi.org/10.1007/978-3-030-99739-7_44. 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 humanities.