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    <titleInfo>
        <title>Transfer Learning for Czech Historical Named Entity Recognition</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Helena</namePart>
        <namePart type="family">Hubková</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Pavel</namePart>
        <namePart type="family">Kral</namePart>
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        <dateIssued>2021-09</dateIssued>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>INCOMA Ltd.</publisher>
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    <abstract>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 This is particularly evident in the historical document processing field.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.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.65</url>
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    <part>
        <date>2021-09</date>
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            <start>576</start>
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%0 Conference Proceedings
%T Transfer Learning for Czech Historical Named Entity Recognition
%A Hubková, Helena
%A Kral, Pavel
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F hubkova-kral-2021-transfer
%X 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.
%U https://aclanthology.org/2021.ranlp-1.65
%P 576-582

Markdown (Informal)

[Transfer Learning for Czech Historical Named Entity Recognition](https://aclanthology.org/2021.ranlp-1.65) (Hubková & Kral, RANLP 2021)

• Transfer Learning for Czech Historical Named Entity Recognition (Hubková & Kral, RANLP 2021)

ACL

• Helena Hubková and Pavel Kral. 2021. Transfer Learning for Czech Historical Named Entity Recognition. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 576–582, Held Online. INCOMA Ltd..