@inproceedings{liebeskind-etal-2026-domain,
title = "Domain-Aware Error Correction for Citation {NER} in Medieval {H}ebrew Responsa",
author = "LIebeskind, Shmuel and
Zhitomirsky-Geffet, Maayan and
Katzoff, Binyamin and
Ben-Gigi, Nati and
Schler, Jonathan",
editor = "Sprugnoli, Rachele and
Passarotti, Marco",
booktitle = "Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages ({LT}4{HALA} 2026) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.lt4hala-1.9/",
doi = "10.63317/5euewaq3i8b5",
pages = "96--105",
abstract = "Citation identification in historical and ancient texts poses challenges that extend beyond surface-level pattern recognition, including implicit references, morphological fusion, and discourse-driven ambiguity. In this work, we address citation Named Entity Recognition (NER) in medieval Hebrew Responsa literature using a modular, LLM-based correction pipeline. Rather than treating large language models as end-to-end predictors, we leverage them as structured components: an initial prompt-based expert tagger, complementary LLM judges for systematic error detection, and domain-aware correction grounded in philological regularities. Our approach requires no end-to-end fine-tuning and only minimal labeled supervision (a small validation set for training a lightweight error-detection classifier), narrowing the performance gap to strong supervised models trained on domain-specific data. The results suggest that explicit error handling and interpretability-driven design offer a promising direction for historical NLP in low-resource settings."
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<abstract>Citation identification in historical and ancient texts poses challenges that extend beyond surface-level pattern recognition, including implicit references, morphological fusion, and discourse-driven ambiguity. In this work, we address citation Named Entity Recognition (NER) in medieval Hebrew Responsa literature using a modular, LLM-based correction pipeline. Rather than treating large language models as end-to-end predictors, we leverage them as structured components: an initial prompt-based expert tagger, complementary LLM judges for systematic error detection, and domain-aware correction grounded in philological regularities. Our approach requires no end-to-end fine-tuning and only minimal labeled supervision (a small validation set for training a lightweight error-detection classifier), narrowing the performance gap to strong supervised models trained on domain-specific data. The results suggest that explicit error handling and interpretability-driven design offer a promising direction for historical NLP in low-resource settings.</abstract>
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%0 Conference Proceedings
%T Domain-Aware Error Correction for Citation NER in Medieval Hebrew Responsa
%A LIebeskind, Shmuel
%A Zhitomirsky-Geffet, Maayan
%A Katzoff, Binyamin
%A Ben-Gigi, Nati
%A Schler, Jonathan
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F liebeskind-etal-2026-domain
%X Citation identification in historical and ancient texts poses challenges that extend beyond surface-level pattern recognition, including implicit references, morphological fusion, and discourse-driven ambiguity. In this work, we address citation Named Entity Recognition (NER) in medieval Hebrew Responsa literature using a modular, LLM-based correction pipeline. Rather than treating large language models as end-to-end predictors, we leverage them as structured components: an initial prompt-based expert tagger, complementary LLM judges for systematic error detection, and domain-aware correction grounded in philological regularities. Our approach requires no end-to-end fine-tuning and only minimal labeled supervision (a small validation set for training a lightweight error-detection classifier), narrowing the performance gap to strong supervised models trained on domain-specific data. The results suggest that explicit error handling and interpretability-driven design offer a promising direction for historical NLP in low-resource settings.
%R 10.63317/5euewaq3i8b5
%U https://aclanthology.org/2026.lt4hala-1.9/
%U https://doi.org/10.63317/5euewaq3i8b5
%P 96-105
Markdown (Informal)
[Domain-Aware Error Correction for Citation NER in Medieval Hebrew Responsa](https://aclanthology.org/2026.lt4hala-1.9/) (LIebeskind et al., LT4HALA 2026)
ACL
- Shmuel LIebeskind, Maayan Zhitomirsky-Geffet, Binyamin Katzoff, Nati Ben-Gigi, and Jonathan Schler. 2026. Domain-Aware Error Correction for Citation NER in Medieval Hebrew Responsa. In Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026, pages 96–105, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).