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

TL;DR

The paper presents a multilingual, historical text relation extraction challenge focusing on person-place associations, emphasizing accuracy, efficiency, and domain generalization to aid digital humanities applications.

Contribution

It extends previous campaigns by introducing semantic relation extraction with temporal and geographical reasoning in a multilingual, historical context.

Findings

Evaluation of systems on accuracy, efficiency, and generalization

Development of methods for temporal and geographical reasoning

Support for downstream digital humanities applications

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.