--- title: "Who cites HeBERT & HebEMO: a Hebrew BERT Model and a Tool…" description: "24 papers of the Elamur Research library cite «HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition», found in…" canonical: "https://research.elamur.ai/free/en/papers/chriqui-2021-hebert-hebemo-hebrew-bert-model/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/chriqui-2021-hebert-hebemo-hebrew-bert-model/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [HeBERT & HebEMO: a Hebrew BERT Model and a Tool…](https://md.research.elamur.ai/free/en/papers/chriqui-2021-hebert-hebemo-hebrew-bert-model/) / cited by # Who cites «HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition» 24 papers of our library cite it (of the 140,884 whose bibliographies we read) · 19 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/chriqui-2021-hebert-hebemo-hebrew-bert-model/) 1. [**Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,548 citations*](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/) 2. [**Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets**Julia Kreutzer, Isaac Caswell, Lisa Wang, et al.*2021 · 447 citations*](https://md.research.elamur.ai/free/en/papers/kreutzer-2021-quality-glance-audit-web-crawled/) 3. [**AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level**Amit Seker, Elron Bandel, Dan Bareket, et al.*2022 · 57 citations*](https://md.research.elamur.ai/free/en/papers/seker-2022-alephbert-language-model-pre-training/) 4. [**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/) 5. [**Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All**Eylon Gueta, Avi Shmidman, Shaltiel Shmidman, et al.*2022 · 28 citations*](https://md.research.elamur.ai/free/en/papers/gueta-2022-large-pre-trained-models-extra/) 6. [**HeRo: RoBERTa and Longformer Hebrew Language Models**Vitaly Shalumov, Harel Haskey*2023 · 15 citations*](https://md.research.elamur.ai/free/en/papers/shalumov-2023-hero-roberta-longformer-hebrew-language/) 7. [**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/) 8. [**Offensive Hebrew Corpus and Detection using BERT**Nagham Hamad, Mustafa Jarrar, Mohammad Khalilia, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/hamad-2023-offensive-hebrew-corpus-detection-using/) 9. [**Multilingual Sequence-to-Sequence Models for Hebrew NLP**Matan Eyal, Hila Noga, Roee Aharoni, et al.*2023 · 9 citations*](https://md.research.elamur.ai/free/en/papers/eyal-2023-multilingual-sequence-sequence-models-hebrew/) 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. [**A Dataset for Metaphor Detection in Early Medieval Hebrew Poetry**Michael Toker, Oren Mishali, Ophir Münz-Manor, et al.*2024 · 3 citations*](https://md.research.elamur.ai/free/en/papers/toker-2024-dataset-metaphor-detection-early-medieval/) 12. [**Automatic Text Segmentation of Ancient and Historic Hebrew**Elisha Rosensweig, Benjamin Resnick, Hillel Gershuni, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/rosensweig-2025-automatic-text-segmentation-ancient-historic/) 13. [**From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in Hebrew**Chaya Liebeskind, Marina Litvak, Natalia Vanetik*2024 · 2 citations*](https://md.research.elamur.ai/free/en/papers/liebeskind-2024-linguistics-practice-case-study-offensive/) 14. [**Temporal Relation Classification in Hebrew**Guy Yanko, Shahaf Pariente, Kfir Bar*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/yanko-2023-temporal-relation-classification-hebrew/) 15. [**A Classifier of Word-Level Variants in Witnesses of Biblical Hebrew Manuscripts**Iglika Nikolova-Stoupak, Maxime Amblard, Sophie Robert-Hayek, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/stoupak-2025-classifier-word-level-variants-witnesses/) 16. [**Restoring Missing Spaces in Scraped Hebrew Social Media**Avi Shmidman, Shaltiel Shmidman*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/shmidman-2025-restoring-missing-spaces-scraped-hebrew/) 17. [**Towards Safer Hebrew Communication: A Dataset for Offensive Language Detoxification**Natalia Vanetik, Lior Liberov, Marina Litvak, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/vanetik-2025-safer-hebrew-communication-dataset-offensive/) 18. [**Detection of Negative Campaign in Israeli Municipal Elections**Marina Litvak, Natalia Vanetik, Sagiv Talker, et al.*2022 · 1 citation*](https://md.research.elamur.ai/free/en/papers/litvak-2022-detection-negative-campaign-israeli-municipal/) 19. [**HEBATRON: A Hebrew-Specialized Open-Weight Mixture-of-Experts Language Model**Noam Kayzer, Dan Revital, Ori Bar Joseph, et al.*2026*](https://md.research.elamur.ai/free/en/papers/kayzer-2026-hebatron-hebrew-specialized-open-weight/) 5 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/chriqui-2021-hebert-hebemo-hebrew-bert-model/) — HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition