mlsanityData to 7 Oct 2026

Neural Segmental Hypergraphs for Overlapping Mention Recognition

arXiv 1810.01817 ↗First posted 3 Oct 2018cs.CLcited by 32

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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.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2018.

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Citing papers per year: 2021: 13; 2022: 9; 2023: 2; 2024: 4; 2025: 3; 2026: 1.

32 papers.

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  1. 2021A Unified Generative Framework for Various NER Subtaskscited by 141
  2. 2021Unified Named Entity Recognition as Word-Word Relation Classificationcited by 89
  3. 2021A Sequence-to-Set Network for Nested Named Entity Recognitioncited by 40
  4. 2021A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognitioncited by 33
  5. 2021Better Feature Integration for Named Entity Recognitioncited by 22
  6. 2021Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networkscited by 21
  7. 2022Parallel Instance Query Network for Named Entity Recognitioncited by 19
  8. 2021Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognitioncited by 18
  9. 2022Nested Named Entity Recognition as Latent Lexicalized Constituency Parsingcited by 14
  10. 2021Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encodercited by 8
  11. 2022TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tagscited by 8
  12. 2024ToNER: Type-oriented Named Entity Recognition with Generative Language Modelcited by 8
20 more
  1. 2021Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Modelcited by 7
  2. 2022An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognitioncited by 7
  3. 2023A Survey on Semantic Processing Techniquescited by 4
  4. 2021Enhanced Language Representation with Label Knowledge for Span Extractioncited by 3
  5. 2022A dynamic programming algorithm for span-based nested named-entity recognition in O(n^2)cited by 3
  6. 2021Named Entity Recognition in the Style of Object Detectioncited by 2
  7. 2021BoningKnife: Joint Entity Mention Detection and Typing for Nested NER via prior Boundary Knowledgecited by 2
  8. 2022Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challengescited by 2
  9. 2022Few-Shot Nested Named Entity Recognitioncited by 2
  10. 2024CMNER: A Chinese Multimodal NER Dataset based on Social Mediacited by 2
  11. 2024Label Alignment and Reassignment with Generalist Large Language Model for Enhanced Cross-Domain Named Entity Recognitioncited by 1
  12. 2025NameTag 3: A Tool and a Service for Multilingual/Multitagset NERcited by 1
  13. 2021Effect of depth order on iterative nested named entity recognition modelscited by 0
  14. 2022Nested Named Entity Recognition as Holistic Structure Parsingcited by 0
  15. 2022Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopescited by 0
  16. 2023S2F-NER: Exploring Sequence-to-Forest Generation for Complex Entity Recognitioncited by 0
  17. 2024TriG-NER: Triplet-Grid Framework for Discontinuous Named Entity Recognitioncited by 0
  18. 2025EIoU-EMC: A Novel Loss for Domain-specific Nested Entity Recognitioncited by 0
  19. 2025SEDA: A Self-Adapted Entity-Centric Data Augmentation for Boosting Gird-based Discontinuous NER Modelscited by 0
  20. 2026Lexicalized Constituency Parsing for Middle Dutch: Low-resource Training and Cross-Domain Generalizationcited by 0