1 citations · 1 across the 4 of their papers we have counts for
12 papers
ICDAR 2026 HIPE-OCRepair Competition on LLM-Assisted OCR Post-Correction for Historical Documents
Maud Ehrmann, Emanuela Boros, Juri Opitz +3
We present the results of HIPE-OCRepair-2026, an ICDAR competition on LLM-assisted OCR post-correction of historical documents. OCR post-correction remains a long-standing challeng…
ALEE: Any-Language Evaluation of Embeddings via English-Centric Minimal Pairs
Andrianos Michail, Stylianos Psychias, Michelle Wastl +3
Text embeddings are standard for semantic similarity tasks, yet their evaluation remains an open challenge. Current benchmarks are static, cover only a limited set of languages, ar…
CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts
Juri Opitz, Corina Raclé, Emanuela Boros +4
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 ex…
Overview of HIPE-2026: Person-Place Relation Extraction from Multilingual Historical Texts
Juri Opitz, Maud Ehrmann, Corina Raclé +3
Was this person ever at that place, and if so, when? Answering such questions from noisy, multilingual historical documents is the central challenge of HIPE-2026, the third edition…
Attention Calibration for Position-Fair Dense Information Retrieval
Andrianos Michail, Elias Schuhmacher, Juri Opitz +2
Dense retrieval models exhibit positional bias: retrieval effectiveness degrades when relevant information appears later in a passage (Zeng et al., 2025). We ask whether this bias…
Information Representation Fairness in Long-Document Embeddings: The Peculiar Interaction of Positional and Language Bias
Elias Schuhmacher, Andrianos Michail, Juri Opitz +2
To be discoverable in an embedding-based search process, each part of a document should be reflected in its embedding representation. To quantify any potential reflection biases, w…