Transfer Learning for Czech Historical Named Entity Recognition

2021-09-01 · RANLP 2021 9 · Helena Hubková, Pavel Kral

Nowadays, named entity recognition (NER) achieved excellent results on the standard corpora. However, big issues are emerging with a need for an application in a specific domain, because it requires a suitable annotated corpus with adapted NE tag-set. This is particularly evident in the historical document processing field. The main goal of this paper consists of proposing and evaluation of several transfer learning methods to increase the score of the Czech historical NER. We study several information sources, and we use two neural nets for NE modeling and recognition. We employ two corpora for evaluation of our transfer learning methods, namely Czech named entity corpus and Czech historical named entity corpus. We show that BERT representation with fine-tuning and only the simple classifier trained on the union of corpora achieves excellent results.

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Tasks

named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERTAGTransfer Learning

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As the number of digitized archival documents increases very rapidly, named entity recognition (NER) in historical documents has become very important for information extraction and data mining. For this task an annotate…

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Although pre-trained named entity recognition (NER) models are highly accurate on modern corpora, they underperform on historical texts due to differences in language OCR errors. In this work, we develop a new NER corpus…

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