# W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition

arXiv: https://arxiv.org/abs/2305.18624 · HTML: https://mlsanity.com/paper/2305.18624/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [A Theoretical Analysis of Contrastive Unsupervised Representation Learning](https://mlsanity.com/paper/1902.09229/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings](https://mlsanity.com/paper/2204.10298/) (2022)
- [Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network](https://mlsanity.com/paper/2006.05702/) (2020)
- [Equivariant Contrastive Learning](https://mlsanity.com/paper/2111.00899/) (2021)
- [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)
- [Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification](https://mlsanity.com/paper/2109.05427/) (2021)
- [Drug Repurposing for COVID-19 via Knowledge Graph Completion](https://mlsanity.com/paper/2010.09600/) (2020)
- [Label-Agnostic Sequence Labeling by Copying Nearest Neighbors](https://mlsanity.com/paper/1906.04225/) (2019)
- [Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2208.14565/) (2022)
- [Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes](https://mlsanity.com/paper/2208.08023/) (2022)
- [Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks](https://mlsanity.com/paper/2109.09366/) (2021)
- [A Hierarchical N-Gram Framework for Zero-Shot Link Prediction](https://mlsanity.com/paper/2204.10293/) (2022)
- [Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies](https://mlsanity.com/paper/1605.00029/) (2016)
- [Multi-Fusion Chinese WordNet (MCW) : Compound of Machine Learning and Manual Correction](https://mlsanity.com/paper/2002.01761/) (2020)
- [Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text](https://mlsanity.com/paper/2009.07780/) (2020)

## Built on by (most cited first, 4 of 4)

- [How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain](https://mlsanity.com/paper/2307.00186/) (2023)
- [A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare](https://mlsanity.com/paper/2310.18354/) (2023)
- [Research on Medical Named Entity Identification Based On Prompt-Biomrc Model and Its Application in Intelligent Consultation System](https://mlsanity.com/paper/2506.01961/) (2025)
- [Curation and Extraction of Drug-Related Entities from Reddit Platform](https://mlsanity.com/paper/2605.26445/) (2026)

## Abstract (arXiv)

Contrastive learning has become a popular solution for few-shot Name Entity Recognization (NER). The conventional configuration strives to reduce the distance between tokens with the same labels and increase the distance between tokens with different labels. The effect of this setup may, however, in the medical domain, there are a lot of entities annotated as OUTSIDE (O), and they are undesirably pushed apart to other entities that are not labeled as OUTSIDE (O) by the current contrastive learning method end up with a noisy prototype for the semantic representation of the label, though there are many OUTSIDE (O) labeled entities are relevant to the labeled entities. To address this challenge, we propose a novel method named Weighted Prototypical Contrastive Learning for Medical Few Shot Named Entity Recognization (W-PROCER). Our approach primarily revolves around constructing the prototype-based contractive loss and weighting network. These components play a crucial role in assisting the model in differentiating the negative samples from OUTSIDE (O) tokens and enhancing the discrimination ability of contrastive learning. Experimental results show that our proposed W-PROCER framework significantly outperforms the strong baselines on the three medical benchmark datasets.