# Learning to Recognize Discontiguous Entities

arXiv: https://arxiv.org/abs/1810.08579 · HTML: https://mlsanity.com/paper/1810.08579/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2018.

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

- [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)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [Discontinuous Named Entity Recognition as Maximal Clique Discovery](https://mlsanity.com/paper/2106.00218/) (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)
- [An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition](https://mlsanity.com/paper/2208.04534/) (2022)
- [A fast and sound tagging method for discontinuous named-entity recognition](https://mlsanity.com/paper/2409.16243/) (2024)
- [SPaR.txt, a cheap Shallow Parsing approach for Regulatory texts](https://mlsanity.com/paper/2110.01295/) (2021)
- [Label Alignment and Reassignment with Generalist Large Language Model for Enhanced Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2407.17344/) (2024)
- [TriG-NER: Triplet-Grid Framework for Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2411.01839/) (2024)

## Abstract (arXiv)

This paper focuses on the study of recognizing discontiguous entities. Motivated by a previous work, we propose to use a novel hypergraph representation to jointly encode discontiguous entities of unbounded length, which can overlap with one another. To compare with existing approaches, we first formally introduce the notion of model ambiguity, which defines the difficulty level of interpreting the outputs of a model, and then formally analyze the theoretical advantages of our model over previous existing approaches based on linear-chain CRFs. Our empirical results also show that our model is able to achieve significantly better results when evaluated on standard data with many discontiguous entities.