---
title: "Who cites What's Wrong with Hebrew NLP? And How to Make it…"
description: "20 papers of the Elamur Research library cite «What's Wrong with Hebrew NLP? And How to Make it Right», found in their bibliographies; the most cited…"
canonical: "https://research.elamur.ai/free/en/papers/tsarfaty-2019-what-s-wrong-hebrew-nlp/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [What's Wrong with Hebrew NLP? And How to Make it…](https://md.research.elamur.ai/free/en/papers/tsarfaty-2019-what-s-wrong-hebrew-nlp/) / cited by

# Who cites «What's Wrong with Hebrew NLP? And How to Make it Right»

20 papers of our library cite it (of the 140,884 whose bibliographies we read) · 14 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/tsarfaty-2019-what-s-wrong-hebrew-nlp/)

1. [**AlephBERT: A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With**Amit Seker, Elron Bandel, Dan Bareket, et al.*2021 · 56 citations*](https://md.research.elamur.ai/free/en/papers/seker-2021-alephbert-hebrew-large-pre-trained/)
2. [**Neural Modeling for Named Entities and Morphology (NEMO2)**Dan Bareket, Reut Tsarfaty*2021 · 32 citations*](https://md.research.elamur.ai/free/en/papers/bareket-2021-neural-modeling-named-entities-morphology/)
3. [**Toward a Period-Specific Optimized Neural Network for OCR Error Correction of Historical Hebrew Texts**Omri Suissa, Maayan Zhitomirsky-Geffet, Avshalom Elmalech*2023 · 13 citations*](https://md.research.elamur.ai/free/en/papers/suissa-2023-toward-period-specific-optimized-neural/)
4. [**Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language**Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, et al.*2022 · 13 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2022-introducing-berel-bert-embeddings-rabbinic/)
5. [**Restoring Hebrew Diacritics Without a Dictionary**Elazar Gershuni, Yuval Pinter*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/gershuni-2022-restoring-hebrew-diacritics-without-dictionary/)
6. [**MRL Parsing Without Tears: The Case of Hebrew**Shaltiel Shmidman, Avi Shmidman, Moshe Koppel, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2024-mrl-parsing-without-tears-case/)
7. [**HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew**Tzuf Paz-Argaman, Itai Mondshine, Asaf Achi Mordechai, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/argaman-2024-hesum-novel-dataset-abstractive-text/)
8. [**Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew**Eylon Gueta, Omer Goldman, Reut Tsarfaty*2023 · 7 citations*](https://md.research.elamur.ai/free/en/papers/gueta-2023-explicit-morphological-knowledge-improves-pre/)
9. [**A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration**Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, et al.*2020 · 6 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2020-novel-challenge-set-hebrew-morphological/)
10. [**Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?**Avi Shmidman, Cheyn Shmuel Shmidman, Dan Bareket, et al.*2023 · 5 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2023-do-pretrained-contextual-language-models/)
11. [**HeGeL: A Novel Dataset for Geo-Location from Hebrew Text**Tzuf Paz-Argaman, Tal Bauman, Itai Mondshine, et al.*2023 · 4 citations*](https://md.research.elamur.ai/free/en/papers/argaman-2023-hegel-novel-dataset-geo-location/)
12. [**MTQE.en-he: Machine Translation Quality Estimation for English-Hebrew**Andy Rosenbaum, Assaf Siani, Ilan Kernerman*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/rosenbaum-2026-mtqe-en-he-machine-translation/)
13. [**The Truth, The Whole Truth, and Nothing but the Truth: A New Benchmark Dataset for Hebrew Text Credibility Assessment**Ben Hagag, Reut Tsarfaty*2023*](https://md.research.elamur.ai/free/en/papers/hagag-2023-truth-whole-truth-nothing-but/)
14. [**NLP in the DH pipeline: Transfer-learning to a Chronolect**Aynat Rubinstein, Avi Shmidman*2021*](https://md.research.elamur.ai/free/en/papers/rubinstein-2021-nlp-dh-pipeline-transfer-learning/)

6 more papers of our library cite it; they have no page here yet.

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