# Data Centric Domain Adaptation for Historical Text with OCR Errors

arXiv: https://arxiv.org/abs/2107.00927 · HTML: https://mlsanity.com/paper/2107.00927/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 2 Jul 2021.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [Cloze-driven Pretraining of Self-attention Networks](https://mlsanity.com/paper/1903.07785/) (2019)
- [Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking](https://mlsanity.com/paper/1904.06707/) (2019)
- [Misspelling Oblivious Word Embeddings](https://mlsanity.com/paper/1905.09755/) (2019)
- [Towards Robust Named Entity Recognition for Historic German](https://mlsanity.com/paper/1906.07592/) (2019)

## Abstract (arXiv)

We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French. For the cross-domain case, we address domain shift by integrating unsupervised in-domain data via contextualized string embeddings; and OCR errors by injecting synthetic OCR errors into the source domain and address data centric domain adaptation. We propose a general approach to imitate OCR errors in arbitrary input data. Our cross-domain as well as our in-domain results outperform several strong baselines and establish state-of-the-art results. We publish preprocessed versions of the French and Dutch Europeana NER corpora.