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# Domain-Aware Error Correction for Citation NER in Medieval Hebrew Responsa

**Authors:** Shmuel Liebeskind; [Maayan Zhitomirsky-Geffet](https://md.mova.elamur.ai/free/en/docs/papers/authors/maayan-zhitomirsky-geffet/); Binyamin Katzoff; Nati Ben-Gigi; [Jonathan Schler](https://md.mova.elamur.ai/free/en/docs/papers/authors/jonathan-schler/) · **Year:** 2026 · **Venue:** LT4HALA 2026 @ LREC 2026 **Link:** [aclanthology.org/2026.lt4hala-1.9](https://aclanthology.org/2026.lt4hala-1.9/) · library `aclbulk-2026-liebeskind-domain-aware-error-correction-citation` · **Licence:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — summary only

[**0** citations](https://openalex.org/works?filter=cites:W7166523132) openalex · 2026-10-18[Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026](https://aclanthology.org/volumes/2026.lt4hala-1/)[licence](https://creativecommons.org/licenses/by-nc/4.0/)

- [Publisher's page](https://aclanthology.org/2026.lt4hala-1.9/)
- [PDF](https://aclanthology.org/2026.lt4hala-1.9.pdf)
- [DOI 10.63317/5euewaq3i8b5](https://doi.org/10.63317/5euewaq3i8b5)
- [OpenAlex](https://openalex.org/W7166523132)

**Authors (5)**

1. [Shmuel LIebeskind](https://openalex.org/A5011215522) [ORCID](https://orcid.org/0009-0006-3205-7038) · h 6 · 79 citations · 12 works
2. Maayan Zhitomirsky-Geffet
3. [Binyamin Katzoff](https://openalex.org/A5028193104) [ORCID](https://orcid.org/0000-0001-5199-6213) · h 2 · 9 citations · 14 works
4. Nati Ben-Gigi
5. [Jonathan Schler](https://openalex.org/A5071610277) [ORCID](https://orcid.org/0000-0002-2233-455X) · h 18 · 3551 citations · 59 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The task is citation NER in medieval Hebrew Responsa: each token of a citation gets a role (book name, author, reference number, reference word, and so on, or outside). The authors use an LLM as a tagger driven by a long system prompt, written and refined on the validation set only, that encodes rabbinic abbreviations as lookup anchors and gives priority to reference tokens. Then LLM "judges" flag likely token-level errors, a small gradient-boosting classifier trained on validation data decides which flags to trust, and simple domain rules correct the rest, for example separating quoted lemma text from real citations. Nothing is fine-tuned. On the test set the correction step adds about four F1 points over the prompt baseline, mostly through higher precision, and the few-shot result is 84.4 F1 on test (85.3 on validation). The authors report the system approaching a supervised BEREL-CRF model trained on the domain data.

## Ideas

- I1. A flag-then-decide step after the segmenter in `tools/hbo`: each prefix split is checked by a rule (or by a tiny classifier trained on a held-out slice of MACULA) that flags splits no lexicon entry supports; a flagged split falls back to the unsplit form and the log says why. Measure flag precision and recall against MACULA gold on verses kept out of training.
- I2. A rare-label guard for the tagger: the paper marks rare classes (the adjective class is under 1% of tokens) as rare in the prompt because they are over-predicted. For Mova, compare predicted label rates with the treebank's rates and, when a rare label exceeds its prior, fall back to the default label and record the reason.
- I3. A deterministic table of rabbinic abbreviations (letter pairs with a gershayim, such as the ones for "perek kama" and "amud aleph") as lexicon anchors for the Rabbinic layer, tested on a held-out set of citations.

## For Mova

I1 is a check inside `tools/hbo`, between the segmenter and `gparse`. I2 is a rule for the output of `gparse` in `tools/hbo`, with its measure written up in [13. Biblical Hebrew in Mova: the tools](https://md.mova.elamur.ai/free/en/docs/languages/hbo/13-mova-tools/). I3 goes into [11. Mishnaic Hebrew (hbo-x-mishnaic)](https://md.mova.elamur.ai/free/en/docs/languages/hbo/11-mishnaic/) and is a test for the Rabbinic segments that `tools/langseg` cuts out.

Topics: [Named entity recognition](https://md.mova.elamur.ai/free/en/docs/papers/topics/named-entity-recognition/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.63317/5euewaq3i8b5](https://doi.org/10.63317/5euewaq3i8b5)

```
@inproceedings{liebeskind2026domain,
  title = {{Domain-Aware Error Correction for Citation NER in Medieval Hebrew Responsa}},
  author = {LIebeskind, Shmuel and Zhitomirsky-Geffet, Maayan and Katzoff, Binyamin and Ben-Gigi, Nati and Schler, Jonathan},
  booktitle = {Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026},
  year = {2026},
  doi = {10.63317/5euewaq3i8b5},
  url = {https://doi.org/10.63317/5euewaq3i8b5}
}
```

- [← previous Human-AI Annotation Error Auditing for Hebrew Diacritization with Frontier LLMs](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2026-gershuni-human-ai-annotation-error-auditing/)
- [next → BEReshiT: an Ancient Hebrew Model based on DictaBERT](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2026-stoupak-bereshit-ancient-hebrew-model-based/)
- [↑ back to Ancient Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo/)

## References

16 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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