# Label Semantics for Few Shot Named Entity Recognition

arXiv: https://arxiv.org/abs/2203.08985 · HTML: https://mlsanity.com/paper/2203.08985/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 44 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Muppet: Massive Multi-task Representations with Pre-Finetuning](https://mlsanity.com/paper/2101.11038/) (2021)
- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network](https://mlsanity.com/paper/2006.05702/) (2020)
- [Few-shot classification in Named Entity Recognition Task](https://mlsanity.com/paper/1812.06158/) (2018)
- [Diverse Few-Shot Text Classification with Multiple Metrics](https://mlsanity.com/paper/1805.07513/) (2018)
- [Few-shot Text Classification with Distributional Signatures](https://mlsanity.com/paper/1908.06039/) (2019)
- [Named Entity Recognition without Labelled Data: A Weak Supervision Approach](https://mlsanity.com/paper/2004.14723/) (2020)
- [Few-Shot Named Entity Recognition: A Comprehensive Study](https://mlsanity.com/paper/2012.14978/) (2020)
- [Local Additivity Based Data Augmentation for Semi-supervised NER](https://mlsanity.com/paper/2010.01677/) (2020)
- [Augmented Natural Language for Generative Sequence Labeling](https://mlsanity.com/paper/2009.13272/) (2020)
- [Zero-Shot Open Entity Typing as Type-Compatible Grounding](https://mlsanity.com/paper/1907.03228/) (2019)
- [Personalized Embedding-based e-Commerce Recommendations at eBay](https://mlsanity.com/paper/2102.06156/) (2021)
- [Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas](https://mlsanity.com/paper/2010.11333/) (2020)

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

- [Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://mlsanity.com/paper/2303.08559/) (2023)
- [Label Semantic Aware Pre-training for Few-shot Text Classification](https://mlsanity.com/paper/2204.07128/) (2022)
- [Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2210.06629/) (2022)
- [Prompt-Based Metric Learning for Few-Shot NER](https://mlsanity.com/paper/2211.04337/) (2022)
- [A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER](https://mlsanity.com/paper/2308.14533/) (2023)
- [SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2210.09049/) (2022)
- [Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy](https://mlsanity.com/paper/2305.12802/) (2023)
- [UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective](https://mlsanity.com/paper/2305.10306/) (2023)
- [Learning In-context Learning for Named Entity Recognition](https://mlsanity.com/paper/2305.11038/) (2023)
- [Few-shot Event Detection: An Empirical Study and a Unified View](https://mlsanity.com/paper/2305.01901/) (2023)
- [PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search](https://mlsanity.com/paper/2305.12217/) (2023)
- [DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal](https://mlsanity.com/paper/2309.00855/) (2023)
- [Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2302.06397/) (2023)
- [Pre-Finetuning for Few-Shot Emotional Speech Recognition](https://mlsanity.com/paper/2302.12921/) (2023)
- [Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents](https://mlsanity.com/paper/2210.05613/) (2022)
- [ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/2301.08855/) (2023)
- [TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2306.03974/) (2023)
- [Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling](https://mlsanity.com/paper/2307.07946/) (2023)
- [Taxonomy Expansion for Named Entity Recognition](https://mlsanity.com/paper/2305.13191/) (2023)
- [A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2404.17178/) (2024)
- [Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity](https://mlsanity.com/paper/2507.15864/) (2025)

## Abstract (arXiv)

We study the problem of few shot learning for named entity recognition. Specifically, we leverage the semantic information in the names of the labels as a way of giving the model additional signal and enriched priors. We propose a neural architecture that consists of two BERT encoders, one to encode the document and its tokens and another one to encode each of the labels in natural language format. Our model learns to match the representations of named entities computed by the first encoder with label representations computed by the second encoder. The label semantics signal is shown to support improved state-of-the-art results in multiple few shot NER benchmarks and on-par performance in standard benchmarks. Our model is especially effective in low resource settings.