# A fast and sound tagging method for discontinuous named-entity recognition

arXiv: https://arxiv.org/abs/2409.16243 · HTML: https://mlsanity.com/paper/2409.16243/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 63 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://mlsanity.com/paper/2006.04884/) (2020)
- [Learning with Fenchel-Young Losses](https://mlsanity.com/paper/1901.02324/) (2019)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [Differentiable Dynamic Programming for Structured Prediction and Attention](https://mlsanity.com/paper/1802.03676/) (2018)
- [Torch-Struct: Deep Structured Prediction Library](https://mlsanity.com/paper/2002.00876/) (2020)
- [An Effective Transition-based Model for Discontinuous NER](https://mlsanity.com/paper/2004.13454/) (2020)
- [Discontinuous Named Entity Recognition as Maximal Clique Discovery](https://mlsanity.com/paper/2106.00218/) (2021)
- [Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities](https://mlsanity.com/paper/1909.00930/) (2019)
- [Learning to Recognize Discontiguous Entities](https://mlsanity.com/paper/1810.08579/) (2018)
- [Constraining Linear-chain CRFs to Regular Languages](https://mlsanity.com/paper/2106.07306/) (2021)
- [Decoding with Finite-State Transducers on GPUs](https://mlsanity.com/paper/1701.03038/) (2017)
- [CoRec: An Easy Approach for Coordination Recognition](https://mlsanity.com/paper/2311.18712/) (2023)

## Built on by (most cited first, 4 of 4)

- [Bregman Conditional Random Fields: Sequence Labeling with Parallelizable Inference Algorithms](https://mlsanity.com/paper/2506.00732/) (2025)
- [GapDNER: A Gap-Aware Grid Tagging Model for Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2510.10927/) (2025)
- [SEDA: A Self-Adapted Entity-Centric Data Augmentation for Boosting Gird-based Discontinuous NER Models](https://mlsanity.com/paper/2511.20143/) (2025)
- [A Study on Building Efficient Zero-Shot Relation Extraction Models](https://mlsanity.com/paper/2603.01266/) (2026)

## Abstract (arXiv)

We introduce a novel tagging scheme for discontinuous named entity recognition based on an explicit description of the inner structure of discontinuous mentions. We rely on a weighted finite state automaton for both marginal and maximum a posteriori inference. As such, our method is sound in the sense that (1) well-formedness of predicted tag sequences is ensured via the automaton structure and (2) there is an unambiguous mapping between well-formed sequences of tags and (discontinuous) mentions. We evaluate our approach on three English datasets in the biomedical domain, and report comparable results to state-of-the-art while having a way simpler and faster model.