# Neural Segmental Hypergraphs for Overlapping Mention Recognition

arXiv: https://arxiv.org/abs/1810.01817 · HTML: https://mlsanity.com/paper/1810.01817/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 30 of 32)

- [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 Sequence-to-Set Network for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.08901/) (2021)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [Better Feature Integration for Named Entity Recognition](https://mlsanity.com/paper/2104.05316/) (2021)
- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [Parallel Instance Query Network for Named Entity Recognition](https://mlsanity.com/paper/2203.10545/) (2022)
- [Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition](https://mlsanity.com/paper/2110.07480/) (2021)
- [Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing](https://mlsanity.com/paper/2203.04665/) (2022)
- [Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder](https://mlsanity.com/paper/2109.04500/) (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)
- [ToNER: Type-oriented Named Entity Recognition with Generative Language Model](https://mlsanity.com/paper/2404.09145/) (2024)
- [Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model](https://mlsanity.com/paper/2106.09234/) (2021)
- [An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition](https://mlsanity.com/paper/2208.04534/) (2022)
- [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023)
- [Enhanced Language Representation with Label Knowledge for Span Extraction](https://mlsanity.com/paper/2111.00884/) (2021)
- [A dynamic programming algorithm for span-based nested named-entity recognition in O(n^2)](https://mlsanity.com/paper/2210.04738/) (2022)
- [Named Entity Recognition in the Style of Object Detection](https://mlsanity.com/paper/2101.11122/) (2021)
- [BoningKnife: Joint Entity Mention Detection and Typing for Nested NER via prior Boundary Knowledge](https://mlsanity.com/paper/2107.09429/) (2021)
- [Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challenges](https://mlsanity.com/paper/2211.00301/) (2022)
- [Few-Shot Nested Named Entity Recognition](https://mlsanity.com/paper/2212.00953/) (2022)
- [CMNER: A Chinese Multimodal NER Dataset based on Social Media](https://mlsanity.com/paper/2402.13693/) (2024)
- [Label Alignment and Reassignment with Generalist Large Language Model for Enhanced Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2407.17344/) (2024)
- [NameTag 3: A Tool and a Service for Multilingual/Multitagset NER](https://mlsanity.com/paper/2506.05949/) (2025)
- [Effect of depth order on iterative nested named entity recognition models](https://mlsanity.com/paper/2104.01037/) (2021)
- [Nested Named Entity Recognition as Holistic Structure Parsing](https://mlsanity.com/paper/2204.08006/) (2022)
- [Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes](https://mlsanity.com/paper/2211.10854/) (2022)
- [S2F-NER: Exploring Sequence-to-Forest Generation for Complex Entity Recognition](https://mlsanity.com/paper/2310.18944/) (2023)
- [TriG-NER: Triplet-Grid Framework for Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2411.01839/) (2024)
- [EIoU-EMC: A Novel Loss for Domain-specific Nested Entity Recognition](https://mlsanity.com/paper/2504.14203/) (2025)

## Abstract (arXiv)

In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. We show that our model built on top of such a new representation is able to capture features and interactions that cannot be captured by previous models while maintaining a low time complexity for inference. We also present a theoretical analysis to formally assess how our representation is better than alternative representations reported in the literature in terms of representational power. Coupled with neural networks for feature learning, our model achieves the state-of-the-art performance in three benchmark datasets annotated with overlapping mentions.