# Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields

arXiv: https://arxiv.org/abs/2404.09383 · HTML: https://mlsanity.com/paper/2404.09383/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [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)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks](https://mlsanity.com/paper/1703.06345/) (2017)
- [Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation](https://mlsanity.com/paper/1508.02096/) (2015)
- [Multi-task Domain Adaptation for Sequence Tagging](https://mlsanity.com/paper/1608.02689/) (2016)
- [Cross-lingual Pseudo-Projected Expectation Regularization for Weakly Supervised Learning](https://mlsanity.com/paper/1310.1597/) (2013)

## Built on by (most cited first, 3 of 3)

- [Do "English" Named Entity Recognizers Work Well on Global Englishes?](https://mlsanity.com/paper/2404.13465/) (2024)
- [Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?](https://mlsanity.com/paper/2505.16814/) (2025)
- [HiligayNER: A Baseline Named Entity Recognition Model for Hiligaynon](https://mlsanity.com/paper/2510.10776/) (2025)

## Abstract (arXiv)

Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world's languages, it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low resource languages jointly. Learning character representations for multiple related languages allows transfer among the languages, improving F1 by up to 9.8 points over a loglinear CRF baseline.