# What's Wrong with Hebrew NLP? And How to Make it Right

arXiv: https://arxiv.org/abs/1908.05453 · HTML: https://mlsanity.com/paper/1908.05453/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2019.

## Built on by (most cited first, 16 of 16)

- [AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With](https://mlsanity.com/paper/2104.04052/) (2021)
- [Restoring Hebrew Diacritics Without a Dictionary](https://mlsanity.com/paper/2105.05209/) (2021)
- [HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew](https://mlsanity.com/paper/2406.03897/) (2024)
- [Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://mlsanity.com/paper/2311.00658/) (2023)
- [Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language](https://mlsanity.com/paper/2208.01875/) (2022)
- [HeGeL: A Novel Dataset for Geo-Location from Hebrew Text](https://mlsanity.com/paper/2307.00509/) (2023)
- [MRL Parsing Without Tears: The Case of Hebrew](https://mlsanity.com/paper/2403.06970/) (2024)
- [Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization](https://mlsanity.com/paper/2507.08342/) (2025)
- [Toward a Period-Specific Optimized Neural Network for OCR Error Correction of Historical Hebrew Texts](https://mlsanity.com/paper/2307.16213/) (2023)
- [Critical biblical studies via word frequency analysis: unveiling text authorship](https://mlsanity.com/paper/2410.19883/) (2024)
- [Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?](https://mlsanity.com/paper/2405.07099/) (2024)
- [Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic](https://mlsanity.com/paper/2510.17001/) (2025)
- [Style transfer and classification in hebrew news items](https://mlsanity.com/paper/2212.03019/) (2022)
- [Hebrew Diacritics Restoration using Visual Representation](https://mlsanity.com/paper/2510.26521/) (2025)
- [MTQE.en-he: Machine Translation Quality Estimation for English-Hebrew](https://mlsanity.com/paper/2602.06546/) (2026)
- [The Degree of Language Diacriticity and Its Effect on Tasks](https://mlsanity.com/paper/2603.27653/) (2026)

## Abstract (arXiv)

For languages with simple morphology, such as English, automatic annotation pipelines such as spaCy or Stanford's CoreNLP successfully serve projects in academia and the industry. For many morphologically-rich languages (MRLs), similar pipelines show sub-optimal performance that limits their applicability for text analysis in research and the industry.The sub-optimal performance is mainly due to errors in early morphological disambiguation decisions, which cannot be recovered later in the pipeline, yielding incoherent annotations on the whole. In this paper we describe the design and use of the Onlp suite, a joint morpho-syntactic parsing framework for processing Modern Hebrew texts. The joint inference over morphology and syntax substantially limits error propagation, and leads to high accuracy. Onlp provides rich and expressive output which already serves diverse academic and commercial needs. Its accompanying online demo further serves educational activities, introducing Hebrew NLP intricacies to researchers and non-researchers alike.