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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We use the cased variant from https://huggingface.co/bert-base-multilingual-cased
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Acknowledgement
This work was funded by the European Research Council (ERC #740516).
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A Appendix
Detailed Information About Experiments and Data
The computing infrastructure we use for all our experiments is one GeForce GTX 1080Ti GPU with an average runtime of 12 h per experiment. For the French and Dutch baseline model NN base we count 15,895,683 parameters each. For the French NN ensemble model there are 88,264,777 parameters and 96,895,161 parameters for the Dutch NN ensemble.
The Europeana Newspaper Corpus is split 80/10/10 into train/dev/test (Table 6). The downsampled French WikiNER corpus is split 70/15/15 into train/dev/test and the Dutch CoNLL-02 corpus is already split in its original version. The downloadable version of the data can be found here: https://github.com/stefan-it/historic-domain-adaptation-icdar.
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März, L., Schweter, S., Poerner, N., Roth, B., Schütze, H. (2021). Data Centric Domain Adaptation for Historical Text with OCR Errors. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_48
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