# An Effective Transition-based Model for Discontinuous NER

arXiv: https://arxiv.org/abs/2004.13454 · HTML: https://mlsanity.com/paper/2004.13454/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 30 of 33)

- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents](https://mlsanity.com/paper/2101.11796/) (2021)
- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER](https://mlsanity.com/paper/2203.03903/) (2022)
- [Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction](https://mlsanity.com/paper/2310.11016/) (2023)
- [Discontinuous Named Entity Recognition as Maximal Clique Discovery](https://mlsanity.com/paper/2106.00218/) (2021)
- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
- [Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media](https://mlsanity.com/paper/2109.05815/) (2021)
- [TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags](https://mlsanity.com/paper/2211.00684/) (2022)
- [A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends](https://mlsanity.com/paper/2302.03512/) (2023)
- [ToNER: Type-oriented Named Entity Recognition with Generative Language Model](https://mlsanity.com/paper/2404.09145/) (2024)
- [Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies](https://mlsanity.com/paper/2105.08882/) (2021)
- [CARE: Extracting Experimental Findings From Clinical Literature](https://mlsanity.com/paper/2311.09736/) (2023)
- [P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models](https://mlsanity.com/paper/2405.04960/) (2024)
- [Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition](https://mlsanity.com/paper/2303.10870/) (2023)
- [Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction](https://mlsanity.com/paper/2306.05276/) (2023)
- [A fast and sound tagging method for discontinuous named-entity recognition](https://mlsanity.com/paper/2409.16243/) (2024)
- [To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning](https://mlsanity.com/paper/2208.01299/) (2022)
- [Adverse Event Extraction from Discharge Summaries: A New Dataset, Annotation Scheme, and Initial Findings](https://mlsanity.com/paper/2506.14900/) (2025)
- [Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction](https://mlsanity.com/paper/2305.14660/) (2023)
- [CoRec: An Easy Approach for Coordination Recognition](https://mlsanity.com/paper/2311.18712/) (2023)
- [MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction](https://mlsanity.com/paper/2405.18015/) (2024)
- [CED: Catalog Extraction from Documents](https://mlsanity.com/paper/2304.14662/) (2023)
- [MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction](https://mlsanity.com/paper/2308.06546/) (2023)
- [S2F-NER: Exploring Sequence-to-Forest Generation for Complex Entity Recognition](https://mlsanity.com/paper/2310.18944/) (2023)
- [A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages](https://mlsanity.com/paper/2403.18336/) (2024)
- [TriG-NER: Triplet-Grid Framework for Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2411.01839/) (2024)
- [On Fusing ChatGPT and Ensemble Learning in Discon-tinuous Named Entity Recognition in Health Corpora](https://mlsanity.com/paper/2412.16976/) (2024)

## Abstract (arXiv)

Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans. Conventional sequence tagging techniques encode Markov assumptions that are efficient but preclude recovery of these mentions. We propose a simple, effective transition-based model with generic neural encoding for discontinuous NER. Through extensive experiments on three biomedical data sets, we show that our model can effectively recognize discontinuous mentions without sacrificing the accuracy on continuous mentions.