---
title: "Who cites Representations and Architectures in Neural…"
description: "24 papers of the Elamur Research library cite «Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case…"
canonical: "https://research.elamur.ai/free/en/papers/amram-2018-representations-architectures-neural-sentiment-analysis/cited-by/"
---
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# Who cites «Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew»

24 papers of our library cite it (of the 140,884 whose bibliographies we read) · 13 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/amram-2018-representations-architectures-neural-sentiment-analysis/)

1. [**HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition**Avihay Chriqui, Inbal Yahav*2021 · 68 citations*](https://md.research.elamur.ai/free/en/papers/chriqui-2021-hebert-hebemo-hebrew-bert-model/)
2. [**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/)
3. [**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/)
4. [**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/)
5. [**DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew**Shaltiel Shmidman, Avi Shmidman, Moshe Koppel*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2023-dictabert-state-art-bert-suite/)
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. [**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/)
8. [**Introducing DictaLM — A Large Generative Language Model for Modern Hebrew**Shaltiel Shmidman, Avi Shmidman, Amir David Nissan Cohen, et al.*2023 · 1 citation*](https://md.research.elamur.ai/free/en/papers/shmidman-2023-introducing-dictalm-large-generative-language/)
9. [**Graph convolutional networks for exploring authorship hypotheses**Tom Lippincott*2019 · 1 citation*](https://md.research.elamur.ai/free/en/papers/lippincott-2019-graph-convolutional-networks-exploring-authorship/)
10. [**HalleluBERT: Let Every Token That Has Meaning Bear Its Weight**Raphael Schmitt*2026*](https://md.research.elamur.ai/free/en/papers/schmitt-2026-hallelubert-let-every-token-that/)
11. [**NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew**Shaltiel Shmidman, Avi Shmidman, Moshe Koppel*2025*](https://md.research.elamur.ai/free/en/papers/shmidman-2025-neodictabert-pushing-frontier-bert-models/)
12. [**Systematic Textual Availability of Manuscripts**Hadar Miller, Samuel Londner, Tsvi Kuflik, et al.*2025*](https://md.research.elamur.ai/free/en/papers/miller-2025-systematic-textual-availability-manuscripts/)
13. [**Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language**Vitaly Shalumov, Harel Haskey, Yuval Solaz*2024*](https://md.research.elamur.ai/free/en/papers/shalumov-2024-mevaker-conclusion-extraction-allocation-resources/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/amram-2018-representations-architectures-neural-sentiment-analysis/) — Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew