Data Centric Domain Adaptation for Historical Text with OCR Errors
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.
Top- März, Luisa
- Schweter, Stefan
- Poerner, Nina
- Roth, Benjamin
- Schütze, Hinrich
Shortfacts
Category |
Paper in Conference Proceedings or in Workshop Proceedings (Paper) |
Event Title |
16th International Conference on Document Analysis and Recognition ICDAR 2021 |
Divisions |
Data Mining and Machine Learning |
Subjects |
Kuenstliche Intelligenz Sprachverarbeitung Informatik in Beziehung zu Mensch und Gesellschaft |
Event Location |
Lausanne, Switzerland |
Event Type |
Conference |
Event Dates |
September 5-10, 2021 |
Series Name |
Document Analysis and Recognition – ICDAR 2021 |
ISSN/ISBN |
978-3-030-86331-9 |
Page Range |
pp. 748-761 |
Date |
5 September 2021 |
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