# Few-shot Learning for Named Entity Recognition in Medical Text

arXiv: https://arxiv.org/abs/1811.05468 · HTML: https://mlsanity.com/paper/1811.05468/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2018.

## Built on by (most cited first, 19 of 19)

- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [Are Emergent Abilities in Large Language Models just In-Context Learning?](https://mlsanity.com/paper/2309.01809/) (2023)
- [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021)
- [GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain](https://mlsanity.com/paper/2109.02555/) (2021)
- [Clinical Prompt Learning with Frozen Language Models](https://mlsanity.com/paper/2205.05535/) (2022)
- [Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework](https://mlsanity.com/paper/2109.05357/) (2021)
- [Few-shot learning for medical text: A systematic review](https://mlsanity.com/paper/2204.14081/) (2022)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework](https://mlsanity.com/paper/2305.04401/) (2023)
- [CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain](https://mlsanity.com/paper/2112.08754/) (2021)
- [Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques](https://mlsanity.com/paper/2407.03748/) (2024)
- [Few-Shot Nested Named Entity Recognition](https://mlsanity.com/paper/2212.00953/) (2022)
- [From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts](https://mlsanity.com/paper/2305.04928/) (2023)
- [Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets](https://mlsanity.com/paper/2310.11715/) (2023)
- [Beyond Text: Characterizing Domain Expert Needs in Document Research](https://mlsanity.com/paper/2504.12495/) (2025)
- [Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling](https://mlsanity.com/paper/2312.15550/) (2023)
- [Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition](https://mlsanity.com/paper/2403.16158/) (2024)
- [Extracting OPQRST in Electronic Health Records using Large Language Models with Reasoning](https://mlsanity.com/paper/2509.01885/) (2025)
- [Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints](https://mlsanity.com/paper/2509.01899/) (2025)

## Abstract (arXiv)

Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017). However, these gains rely on the availability of large amounts of annotated examples, without which state-of-the-art performance is rarely achievable. This is especially inconvenient for the many NLP fields where annotated examples are scarce, such as medical text. To improve NLP models in this situation, we evaluate five improvements on named entity recognition (NER) tasks when only ten annotated examples are available: (1) layer-wise initialization with pre-trained weights, (2) hyperparameter tuning, (3) combining pre-training data, (4) custom word embeddings, and (5) optimizing out-of-vocabulary (OOV) words. Experimental results show that the F1 score of 69.3% achievable by state-of-the-art models can be improved to 78.87%.