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arXiv · 2107.00927

Data Centric Domain Adaptation for Historical Text with OCR Errors

Luisa März·Stefan Schweter·Nina Poerner·Benjamin Roth·Hinrich Schütze
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Abstract

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.

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Luisa März, Stefan Schweter, Nina Poerner, Benjamin Roth, Hinrich Schütze. 2021-07-02. Data Centric Domain Adaptation for Historical Text with OCR Errors. https://arxiv.org/abs/2107.00927

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