CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts
2026-02-19 • Artificial Intelligence
Artificial IntelligenceComputation and LanguageInformation Retrieval
AI summaryⓘ
The authors describe HIPE-2026, a challenge for computer systems that aims to find connections between people and places in old, messy texts from different languages and times. Participants must figure out if a person was ever at a place or was at that place around the time the text was written, using clues about time and location. The challenge checks how accurate, fast, and adaptable these systems are. The goal is to help build useful tools for historians and researchers working with large historical datasets.
relation extractionhistorical textsmultilingual processingtemporal reasoninggeographical reasoningknowledge graphdigital humanitiessemantic relationsevaluation labdomain generalization
Authors
Juri Opitz, Corina Raclé, Emanuela Boros, Andrianos Michail, Matteo Romanello, Maud Ehrmann, Simon Clematide
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 humanities.