# Towards Robust Named Entity Recognition for Historic German

arXiv: https://arxiv.org/abs/1906.07592 · HTML: https://mlsanity.com/paper/1906.07592/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

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

- [Named Entity Recognition and Classification on Historical Documents: A Survey](https://mlsanity.com/paper/2109.11406/) (2021)
- [Yes but.. Can ChatGPT Identify Entities in Historical Documents?](https://mlsanity.com/paper/2303.17322/) (2023)
- [Data Centric Domain Adaptation for Historical Text with OCR Errors](https://mlsanity.com/paper/2107.00927/) (2021)

## Abstract (arXiv)

Recent advances in language modeling using deep neural networks have shown that these models learn representations, that vary with the network depth from morphology to semantic relationships like co-reference. We apply pre-trained language models to low-resource named entity recognition for Historic German. We show on a series of experiments that character-based pre-trained language models do not run into trouble when faced with low-resource datasets. Our pre-trained character-based language models improve upon classical CRF-based methods and previous work on Bi-LSTMs by boosting F1 score performance by up to 6%. Our pre-trained language and NER models are publicly available under https://github.com/stefan-it/historic-ner .