# Named Entity Recognition -- Is there a glass ceiling?

arXiv: https://arxiv.org/abs/1910.02403 · HTML: https://mlsanity.com/paper/1910.02403/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [What do we Really Know about State of the Art NER?](https://mlsanity.com/paper/2205.00034/) (2022)
- [Annotation Errors and NER: A Study with OntoNotes 5.0](https://mlsanity.com/paper/2406.19172/) (2024)
- [Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition](https://mlsanity.com/paper/2203.10693/) (2022)
- [YATO: Yet Another deep learning based Text analysis Open toolkit](https://mlsanity.com/paper/2209.13877/) (2022)
- [The Role of Global and Local Context in Named Entity Recognition](https://mlsanity.com/paper/2305.03132/) (2023)
- [CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English](https://mlsanity.com/paper/2405.11865/) (2024)
- [Test Set Quality in Multilingual LLM Evaluation](https://mlsanity.com/paper/2508.02635/) (2025)
- [Protagonists' Tagger in Literary Domain -- New Datasets and a Method for Person Entity Linkage](https://mlsanity.com/paper/2110.01349/) (2021)
- [Towards a Diagnostic and Predictive Evaluation Methodology for Sequence Labeling Tasks](https://mlsanity.com/paper/2602.12759/) (2026)

## Abstract (arXiv)

Recent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard or even impossible to correct? In this paper, we present a detailed analysis of the types of errors in state-of-the-art machine learning (ML) methods. Our study reveals the weak and strong points of the Stanford, CMU, FLAIR, ELMO and BERT models, as well as their shared limitations. We also introduce new techniques for improving annotation, for training processes and for checking a model's quality and stability. Presented results are based on the CoNLL 2003 data set for the English language. A new enriched semantic annotation of errors for this data set and new diagnostic data sets are attached in the supplementary materials.