# Neural Modeling for Named Entities and Morphology (NEMO^2)

arXiv: https://arxiv.org/abs/2007.15620 · HTML: https://mlsanity.com/paper/2007.15620/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 26 of 26)

- [Analyzing Dataset Annotation Quality Management in the Wild](https://mlsanity.com/paper/2307.08153/) (2023)
- [AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With](https://mlsanity.com/paper/2104.04052/) (2021)
- [DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew](https://mlsanity.com/paper/2308.16687/) (2023)
- [HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition](https://mlsanity.com/paper/2102.01909/) (2021)
- [Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All](https://mlsanity.com/paper/2211.15199/) (2022)
- [Breaking Character: Are Subwords Good Enough for MRLs After All?](https://mlsanity.com/paper/2204.04748/) (2022)
- [ParaShoot: A Hebrew Question Answering Dataset](https://mlsanity.com/paper/2109.11314/) (2021)
- [HeRo: RoBERTa and Longformer Hebrew Language Models](https://mlsanity.com/paper/2304.11077/) (2023)
- [HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew](https://mlsanity.com/paper/2406.03897/) (2024)
- [Splintering Nonconcatenative Languages for Better Tokenization](https://mlsanity.com/paper/2503.14433/) (2025)
- [Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://mlsanity.com/paper/2311.00658/) (2023)
- [Neural Token Segmentation for High Token-Internal Complexity](https://mlsanity.com/paper/2203.10845/) (2022)
- [Enriching the NArabizi Treebank: A Multifaceted Approach to Supporting an Under-Resourced Language](https://mlsanity.com/paper/2306.14866/) (2023)
- [Multilingual Sequence-to-Sequence Models for Hebrew NLP](https://mlsanity.com/paper/2212.09682/) (2022)
- [MRL Parsing Without Tears: The Case of Hebrew](https://mlsanity.com/paper/2403.06970/) (2024)
- [OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages](https://mlsanity.com/paper/2412.09587/) (2024)
- [Introducing DictaLM -- A Large Generative Language Model for Modern Hebrew](https://mlsanity.com/paper/2309.14568/) (2023)
- [NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew](https://mlsanity.com/paper/2510.20386/) (2025)
- [HalleluBERT: Let Every Token That Has Meaning Bear Its Weight](https://mlsanity.com/paper/2510.21372/) (2025)
- [Samsung R&D Institute Philippines at WMT 2023](https://mlsanity.com/paper/2310.16322/) (2023)
- [Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language](https://mlsanity.com/paper/2403.09719/) (2024)
- [Incentivizing High-Quality Human Annotations with Golden Questions](https://mlsanity.com/paper/2505.19134/) (2025)
- [Users as Annotators: LLM Preference Learning from Comparison Mode](https://mlsanity.com/paper/2510.13830/) (2025)
- [Beyond Word Boundaries: A Hebrew Coreference Benchmark and an Evaluation Protocol for Morphologically Complex Text](https://mlsanity.com/paper/2604.17108/) (2026)
- [Who Gets Named: Citation Type Predicts Individual Naming by Grounded Language Models, and a Roster Instrument Captures 0.5% of It](https://mlsanity.com/paper/2607.23893/) (2026)
- [LLJ Cards: Best practices for the Use of LLMs as Judges](https://mlsanity.com/paper/2609.24516/) (2026)

## Abstract (arXiv)

Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically-Rich Languages (MRLs) pose a challenge to this basic formulation, as the boundaries of Named Entities do not necessarily coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental questions, namely, what are the basic units to be labeled, and how can these units be detected and classified in realistic settings, i.e., where no gold morphology is available. We empirically investigate these questions on a novel NER benchmark, with parallel tokenlevel and morpheme-level NER annotations, which we develop for Modern Hebrew, a morphologically rich-and-ambiguous language. Our results show that explicitly modeling morphological boundaries leads to improved NER performance, and that a novel hybrid architecture, in which NER precedes and prunes morphological decomposition, greatly outperforms the standard pipeline, where morphological decomposition strictly precedes NER, setting a new performance bar for both Hebrew NER and Hebrew morphological decomposition tasks.