# Transfer Learning for Named-Entity Recognition with Neural Networks

arXiv: https://arxiv.org/abs/1705.06273 · HTML: https://mlsanity.com/paper/1705.06273/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2017.

## Built on by (most cited first, 15 of 15)

- [Cross-Domain Multi-Task Learning for Sequential Sentence Classification in Research Papers](https://mlsanity.com/paper/2102.06008/) (2021)
- [One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER](https://mlsanity.com/paper/2301.10410/) (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)
- [FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition](https://mlsanity.com/paper/2208.11464/) (2022)
- [Transferability of Neural Network Clinical De-identification Systems](https://mlsanity.com/paper/2102.08517/) (2021)
- [Cross-domain Named Entity Recognition via Graph Matching](https://mlsanity.com/paper/2408.00981/) (2024)
- [DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain](https://mlsanity.com/paper/2205.06025/) (2022)
- [Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network](https://mlsanity.com/paper/2105.06083/) (2021)
- [Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach](https://mlsanity.com/paper/2111.09415/) (2021)
- [Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences](https://mlsanity.com/paper/2401.10472/) (2024)
- [Zero-Shot Learning in Named-Entity Recognition with External Knowledge](https://mlsanity.com/paper/2111.07734/) (2021)
- [MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages](https://mlsanity.com/paper/2212.09723/) (2022)
- [Length of Stay prediction for Hospital Management using Domain Adaptation](https://mlsanity.com/paper/2306.16823/) (2023)
- [Is Robustness Transferable across Languages in Multilingual Neural Machine Translation?](https://mlsanity.com/paper/2310.20162/) (2023)
- [Named Entity Recognition in COVID-19 tweets with Entity Knowledge Augmentation](https://mlsanity.com/paper/2510.04001/) (2025)

## Abstract (arXiv)

Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: label scarcity is particularly pronounced for patient note de-identification, which is an instance of NER. In this work, we analyze to what extent transfer learning may address this issue. In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.