# Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition

arXiv: https://arxiv.org/abs/2106.15167 · HTML: https://mlsanity.com/paper/2106.15167/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [NormFace: L2 Hypersphere Embedding for Face Verification](https://mlsanity.com/paper/1704.06369/) (2017)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network](https://mlsanity.com/paper/2006.05702/) (2020)
- [Zero-Shot Transfer Learning for Event Extraction](https://mlsanity.com/paper/1707.01066/) (2017)
- [Few-shot classification in Named Entity Recognition Task](https://mlsanity.com/paper/1812.06158/) (2018)
- [Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection](https://mlsanity.com/paper/1910.11621/) (2019)
- [Low-Resource Name Tagging Learned with Weakly Labeled Data](https://mlsanity.com/paper/1908.09659/) (2019)
- [SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data](https://mlsanity.com/paper/1704.06360/) (2017)
- [Train Once, Test Anywhere: Zero-Shot Learning for Text Classification](https://mlsanity.com/paper/1712.05972/) (2017)
- [CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese](https://mlsanity.com/paper/2001.04351/) (2020)
- [Improving One-Shot Learning through Fusing Side Information](https://mlsanity.com/paper/1710.08347/) (2017)
- [Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition](https://mlsanity.com/paper/1804.09021/) (2018)

## Built on by (most cited first, 21 of 21)

- [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021)
- [Decomposed Meta-Learning for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2204.05751/) (2022)
- [Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning](https://mlsanity.com/paper/2311.03748/) (2023)
- [Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes](https://mlsanity.com/paper/2208.08023/) (2022)
- [Few-shot Named Entity Recognition with Self-describing Networks](https://mlsanity.com/paper/2203.12252/) (2022)
- [Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER](https://mlsanity.com/paper/2210.04676/) (2022)
- [Learning In-context Learning for Named Entity Recognition](https://mlsanity.com/paper/2305.11038/) (2023)
- [Embedding Hallucination for Few-Shot Language Fine-tuning](https://mlsanity.com/paper/2205.01307/) (2022)
- [PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search](https://mlsanity.com/paper/2305.12217/) (2023)
- [Cross-domain Named Entity Recognition via Graph Matching](https://mlsanity.com/paper/2408.00981/) (2024)
- [SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2210.05632/) (2022)
- [MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition](https://mlsanity.com/paper/2310.08298/) (2023)
- [ProtoNER: Few shot Incremental Learning for Named Entity Recognition using Prototypical Networks](https://mlsanity.com/paper/2310.02372/) (2023)
- [Prompt-based Text Entailment for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2211.03039/) (2022)
- [Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition](https://mlsanity.com/paper/2211.03270/) (2022)
- [Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2302.07739/) (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)
- [A Sequence Tagging based Framework for Few-Shot Relation Extraction](https://mlsanity.com/paper/2208.08053/) (2022)
- [Causal Interventions-based Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2305.01914/) (2023)
- [A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods](https://mlsanity.com/paper/2310.19055/) (2023)

## Abstract (arXiv)

Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to identify and classify named entity mentions. Prototypical network shows superior performance on few-shot NER. However, existing prototypical methods fail to differentiate rich semantics in other-class words, which will aggravate overfitting under few shot scenario. To address the issue, we propose a novel model, Mining Undefined Classes from Other-class (MUCO), that can automatically induce different undefined classes from the other class to improve few-shot NER. With these extra-labeled undefined classes, our method will improve the discriminative ability of NER classifier and enhance the understanding of predefined classes with stand-by semantic knowledge. Experimental results demonstrate that our model outperforms five state-of-the-art models in both 1-shot and 5-shots settings on four NER benchmarks. We will release the code upon acceptance. The source code is released on https: //github.com/shuaiwa16/OtherClassNER.git.