Juri Opitz et al. launched CLEF HIPE‑2026 to evaluate person‑place relation extraction from noisy, multilingual historical texts.
The lab builds on previous HIPE campaigns (2020, 2022) and introduces a three‑fold evaluation profile: accuracy, computational efficiency, and domain generalization.
Systems must classify $at$ (historical visits) and $isAt$ (current location at publication time) relations, requiring temporal and geographic reasoning.
Success in HIPE‑2026 will support downstream applications in knowledge‑graph construction, historical biography reconstruction, and spatial analysis in digital humanities.
The registration deadline is 23 April 2026, and the paper was submitted to arXiv on 19 February 2026.
📖 Full Retelling
The recent arXiv preprint, submitted on 19 February 2026, outlines the CLEF HIPE‑2026 evaluation lab focused on person‑place relation extraction from noisy, multilingual historical texts. The paper, authored by Juri Opitz, Corina Raclé, Emanuela Boros, Andrianos Michail, Matteo Romanello, Maud Ehrmann, and Simon Clematide, positions the lab within the CLEF community, with a registration deadline of 23 April 2026, and aims to propel progress in semantic relation extraction by targeting the $at$ and $isAt$ relation types across multiple languages and historical periods. Its motivation stems from the need to support downstream applications such as knowledge‑graph construction, historical biography reconstruction, and spatial analysis in the digital humanities, thereby bridging advances in artificial intelligence and historical research.
The HIPE‑2026 framework builds on earlier HIPE‑2020 and HIPE‑2022 campaigns. Participants are invited to develop systems that not only achieve high accuracy but also demonstrate computational efficiency and robust domain generalization. Evaluation metrics cover three axes—accuracy, speed, and cross‑domain transfer—providing a comprehensive benchmark for researchers tackling temporal and geographic cues in noisy datasets. By framing person‑place relation extraction as a cornerstone task, the lab offers a valuable testbed for methods that could be applied to larger scale historical data processing.
With a modular, multilingual design, the lab encourages solutions that can handle the idiosyncrasies of historical corpora such as OCR errors, archaic spellings, and incomplete records. The authors anticipate that successful systems will benefit the wider heritage and digital humanities communities, informing the creation of richer, more accurate knowledge graphs and fostering new insights into historical narratives.
🏷️ Themes
Artificial Intelligence, Natural Language Processing, Digital Humanities, Historical Text Processing, Evaluation Benchmarks
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Deep Analysis
Why It Matters
The HIPE-2026 lab pushes the frontier of automated extraction of person-place relations from noisy, multilingual historical texts, enabling richer knowledge graphs and more accurate historical biographies. By demanding temporal and geographic reasoning, it supports digital humanities research that relies on precise location and time data.
Context & Background
Continuation of the HIPE series, building on HIPE-2020 and HIPE-2022
Focus on two relation types: at and isAt, requiring temporal and geographic reasoning
Three-fold evaluation covering accuracy, computational efficiency, and domain generalization
What Happens Next
Teams will register by the deadline in April and submit their systems for evaluation. Results will be published at ECIR 2026 and may influence future CLEF labs and downstream applications in historical data analysis.
Frequently Asked Questions
What is the main goal of HIPE-2026?
To evaluate systems that can accurately and efficiently extract person-place relations from multilingual historical texts, supporting knowledge-graph construction and spatial analysis.
How can I participate in the lab?
Teams must register through the official CLEF website before the deadline, then submit their system outputs for the defined tasks.
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.17663 [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 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. Comments: ECIR 2026. CLEF Evaluation Lab. Registration DL: 2026/04/23. Task Homepage at this https URL Subjects: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Information Retrieval (cs.IR) Cite as: arXiv:2602.17663 [cs.AI] (or arXiv:2602.17663v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.17663 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Juri Opitz [ view email ] [v1] Thu, 19 Feb 2026 18:59:44 UTC (175 KB...
