Colavizza, G.

Todorov, K.

English

Transfer learning in Natural Language Processing, mainly in the form of pre-trained language models, has recently delivered substantial gains across a range of tasks. Scholars and practitioners working with OCRed historical corpora are thus increasingly exploring the use of pre-trained language models. Nevertheless, the specific challenges posed by historical documents, including OCR quality and linguistic change, call for a critical assessment of the use of pre-trained language models in this setting. We consider two shared tasks, ICDAR2019 (post-OCR correction) and CLEF-HIPE-2020 (Named Entity Recognition, NER), and systematically assess using pre-trained language models with data in French, German and English. We find that using pre-trained language models helps with NER but less so with post-OCR correction. Pre-trained language models should therefore be used critically when working with OCRed historical corpora. We release our code base, in order to allow replicating our results and testing other pre-trained representations

International Migration, Integration and Social Cohesion online publications

Transfer Learning for Historical Corpora: An Assessment on Post-OCR Correction and Named Entity Recognition

https://pure.uva.nl/ws/files/55701562/long32.pdf

Transfer Learning for Historical Corpora: An Assessment on Post-OCR Correction and Named Entity Recognition

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International Migration, Integration and Social Cohesion online publications