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        <title>A Neural Model for Part-of-Speech Tagging in Historical Texts</title>
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    <name type="personal">
        <namePart type="given">Christian</namePart>
        <namePart type="family">Hardmeier</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>Historical texts are challenging for natural language processing because they differ linguistically from modern texts and because of their lack of orthographical and grammatical standardisation. We use a character-level neural network to build a part-of-speech (POS) tagger that can process historical data directly without requiring a separate spelling normalisation stage. Its performance in a Swedish verb identification and a German POS tagging task is similar to that of a two-stage model. We analyse the performance of this tagger and a more traditional baseline system, discuss some of the remaining problems for tagging historical data and suggest how the flexibility of our neural tagger could be exploited to address diachronic divergences in morphology and syntax in early modern Swedish with the help of data from closely related languages.</abstract>
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        <url>https://aclanthology.org/C16-1088</url>
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        <date>2016-12</date>
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%0 Conference Proceedings
%T A Neural Model for Part-of-Speech Tagging in Historical Texts
%A Hardmeier, Christian
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F hardmeier-2016-neural
%X Historical texts are challenging for natural language processing because they differ linguistically from modern texts and because of their lack of orthographical and grammatical standardisation. We use a character-level neural network to build a part-of-speech (POS) tagger that can process historical data directly without requiring a separate spelling normalisation stage. Its performance in a Swedish verb identification and a German POS tagging task is similar to that of a two-stage model. We analyse the performance of this tagger and a more traditional baseline system, discuss some of the remaining problems for tagging historical data and suggest how the flexibility of our neural tagger could be exploited to address diachronic divergences in morphology and syntax in early modern Swedish with the help of data from closely related languages.
%U https://aclanthology.org/C16-1088
%P 922-931

Markdown (Informal)

[A Neural Model for Part-of-Speech Tagging in Historical Texts](https://aclanthology.org/C16-1088) (Hardmeier, COLING 2016)

• A Neural Model for Part-of-Speech Tagging in Historical Texts (Hardmeier, COLING 2016)

ACL

• Christian Hardmeier. 2016. A Neural Model for Part-of-Speech Tagging in Historical Texts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 922–931, Osaka, Japan. The COLING 2016 Organizing Committee.