# Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study

arXiv: https://arxiv.org/abs/2401.10825 · HTML: https://mlsanity.com/paper/2401.10825/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2024.

## Built on by (most cited first, 20 of 20)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents](https://mlsanity.com/paper/2504.00414/) (2025)
- [Information Extraction from Visually Rich Documents using LLM-based Organization of Documents into Independent Textual Segments](https://mlsanity.com/paper/2505.13535/) (2025)
- [HalluShift++: Bridging Language and Vision through Internal Representation Shifts for Hierarchical Hallucinations in MLLMs](https://mlsanity.com/paper/2512.07687/) (2025)
- [Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs](https://mlsanity.com/paper/2511.04473/) (2025)
- [EL4NER: Ensemble Learning for Named Entity Recognition via Multiple Small-Parameter Large Language Models](https://mlsanity.com/paper/2505.23038/) (2025)
- [Comparative Analysis of Encoder-Based NER and Large Language Models for Skill Extraction from Russian Job Vacancies](https://mlsanity.com/paper/2407.19816/) (2024)
- [Shifting NER into High Gear: The Auto-AdvER Approach](https://mlsanity.com/paper/2412.05655/) (2024)
- [OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets](https://mlsanity.com/paper/2508.01630/) (2025)
- [Exploring prompts to elicit memorization in masked language model-based named entity recognition](https://mlsanity.com/paper/2405.03004/) (2024)
- [PlantDeBERTa: An Open Source Language Model for Plant Science](https://mlsanity.com/paper/2506.08897/) (2025)
- [Effective Multi-Task Learning for Biomedical Named Entity Recognition](https://mlsanity.com/paper/2507.18542/) (2025)
- [MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs](https://mlsanity.com/paper/2508.05502/) (2025)
- [EL-MIA: Quantifying Membership Inference Risks of Sensitive Entities in LLMs](https://mlsanity.com/paper/2511.00192/) (2025)
- [FinMetaMind: A Tech Blueprint on NLQ Systems for Financial Knowledge Search](https://mlsanity.com/paper/2601.17333/) (2026)
- [Retrieval-Augmented Self-Taught Reasoning Model with Adaptive Chain-of-Thought for ASR Named Entity Correction](https://mlsanity.com/paper/2602.12287/) (2026)
- [Supporting Workflow Reproducibility by Linking Bioinformatics Tools across Papers and Executable Code](https://mlsanity.com/paper/2603.08195/) (2026)
- [Just Pass Twice: Efficient Token Classification with LLMs for Zero-Shot NER](https://mlsanity.com/paper/2604.05158/) (2026)
- [Less is More: Lightweight Prompt Compression for Question Answering Applications on Edge Devices](https://mlsanity.com/paper/2606.20571/) (2026)
- [Look What You Made Us Cluster: Hate Narrative Extraction from Reddit Discourse](https://mlsanity.com/paper/2609.37408/) (2026)

## Abstract (arXiv)

Named Entity Recognition seeks to extract substrings within a text that name real-world objects and to determine their type (for example, whether they refer to persons or organizations). In this survey, we first present an overview of recent popular approaches, including advancements in Transformer-based methods and Large Language Models (LLMs) that have not had much coverage in other surveys. In addition, we discuss reinforcement learning and graph-based approaches, highlighting their role in enhancing NER performance. Second, we focus on methods designed for datasets with scarce annotations. Third, we evaluate the performance of the main NER implementations on a variety of datasets with differing characteristics (as regards their domain, their size, and their number of classes). We thus provide a deep comparison of algorithms that have never been considered together. Our experiments shed some light on how the characteristics of datasets affect the behavior of the methods we compare.