# Few-shot Named Entity Recognition with Self-describing Networks

arXiv: https://arxiv.org/abs/2203.12252 · HTML: https://mlsanity.com/paper/2203.12252/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 49 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 28 of 28)

- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [Learning Deep Representations of Fine-grained Visual Descriptions](https://mlsanity.com/paper/1605.05395/) (2016)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network](https://mlsanity.com/paper/2006.05702/) (2020)
- [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021)
- [Learning Named Entity Tagger using Domain-Specific Dictionary](https://mlsanity.com/paper/1809.03599/) (2018)
- [Few-shot classification in Named Entity Recognition Task](https://mlsanity.com/paper/1812.06158/) (2018)
- [CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2109.07589/) (2021)
- [Prompt-Learning for Fine-Grained Entity Typing](https://mlsanity.com/paper/2108.10604/) (2021)
- [Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning](https://mlsanity.com/paper/1906.01378/) (2019)
- [Few-Shot Named Entity Recognition: A Comprehensive Study](https://mlsanity.com/paper/2012.14978/) (2020)
- [Less is more: zero-shot learning from online textual documents with noise suppression](https://mlsanity.com/paper/1604.01146/) (2016)
- [Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model](https://mlsanity.com/paper/2106.04098/) (2021)
- [Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER](https://mlsanity.com/paper/2110.08454/) (2021)
- [Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks](https://mlsanity.com/paper/1906.03783/) (2019)
- [Coarse-to-Fine Pre-training for Named Entity Recognition](https://mlsanity.com/paper/2010.08210/) (2020)
- [De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention](https://mlsanity.com/paper/2106.09233/) (2021)
- [Fine-grained Entity Typing via Label Reasoning](https://mlsanity.com/paper/2109.05744/) (2021)
- [Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2106.15167/) (2021)
- [Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data](https://mlsanity.com/paper/2106.08977/) (2021)
- [Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework](https://mlsanity.com/paper/2109.05357/) (2021)
- [Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens](https://mlsanity.com/paper/1805.02214/) (2018)
- [Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model](https://mlsanity.com/paper/2106.09234/) (2021)
- [A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy](https://mlsanity.com/paper/1905.09135/) (2019)
- [One Model to Recognize Them All: Marginal Distillation from NER Models with Different Tag Sets](https://mlsanity.com/paper/2004.05140/) (2020)

## Built on by (most cited first, 16 of 16)

- [A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking](https://mlsanity.com/paper/2312.11816/) (2023)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition](https://mlsanity.com/paper/2208.11464/) (2022)
- [Learning In-context Learning for Named Entity Recognition](https://mlsanity.com/paper/2305.11038/) (2023)
- [Less than One-shot: Named Entity Recognition via Extremely Weak Supervision](https://mlsanity.com/paper/2311.02861/) (2023)
- [Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers](https://mlsanity.com/paper/2210.11265/) (2022)
- [Sentence-to-Label Generation Framework for Multi-task Learning of Japanese Sentence Classification and Named Entity Recognition](https://mlsanity.com/paper/2306.15978/) (2023)
- [In-Context Learning for Few-Shot Nested Named Entity Recognition](https://mlsanity.com/paper/2402.01182/) (2024)
- [TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2306.03974/) (2023)
- [Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2403.14222/) (2024)
- [DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking](https://mlsanity.com/paper/2404.04818/) (2024)
- [Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing](https://mlsanity.com/paper/2302.02275/) (2023)
- [Out-of-Distribution Generalization in Text Classification: Past, Present, and Future](https://mlsanity.com/paper/2305.14104/) (2023)
- [Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling](https://mlsanity.com/paper/2307.07946/) (2023)
- [Few-shot Named Entity Recognition via Superposition Concept Discrimination](https://mlsanity.com/paper/2403.16463/) (2024)
- [Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks](https://mlsanity.com/paper/2307.10291/) (2023)

## Abstract (arXiv)

Few-shot NER needs to effectively capture information from limited instances and transfer useful knowledge from external resources. In this paper, we propose a self-describing mechanism for few-shot NER, which can effectively leverage illustrative instances and precisely transfer knowledge from external resources by describing both entity types and mentions using a universal concept set. Specifically, we design Self-describing Networks (SDNet), a Seq2Seq generation model which can universally describe mentions using concepts, automatically map novel entity types to concepts, and adaptively recognize entities on-demand. We pre-train SDNet with large-scale corpus, and conduct experiments on 8 benchmarks from different domains. Experiments show that SDNet achieves competitive performances on all benchmarks and achieves the new state-of-the-art on 6 benchmarks, which demonstrates its effectiveness and robustness.