A Neural Model for Part-of-Speech Tagging in Historical Texts

Christian Hardmeier

A Neural Model for Part-of-Speech Tagging in Historical Texts

Christian Hardmeier

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

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.

Original language	English
Title of host publication	Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Place of Publication	Osaka, Japan
Publisher	The COLING 2016 Organizing Committee
Pages	922-931
Number of pages	10
ISBN (Electronic)	978-4-87974-702-0
Publication status	Published - 16 Dec 2016
Event	26th International Conference on Computational Linguistics - Osaka, Japan Duration: 11 Dec 2016 → 16 Dec 2016 http://coling2016.anlp.jp/

Conference

Conference	26th International Conference on Computational Linguistics
Abbreviated title	COLING 2016
Country/Territory	Japan
City	Osaka
Period	11/12/16 → 16/12/16
Internet address	http://coling2016.anlp.jp/

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A Neural Modal_HARDMEIER_DOA_20092016_VOR_CC-BYFinal published version, 150 KBLicence: Creative Commons: Attribution (CC-BY)

https://www.aclweb.org/anthology/C16-1088Licence: Creative Commons: Attribution (CC-BY)

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@inproceedings{bba4394027374ab7ba6c575aa098acdf,

title = "A Neural Model for Part-of-Speech Tagging in Historical Texts",

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.",

author = "Christian Hardmeier",

year = "2016",

month = dec,

day = "16",

language = "English",

pages = "922--931",

booktitle = "Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers",

publisher = "The COLING 2016 Organizing Committee",

note = "26th International Conference on Computational Linguistics, COLING 2016 ; Conference date: 11-12-2016 Through 16-12-2016",

url = "http://coling2016.anlp.jp/",

}

Hardmeier, C 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. The COLING 2016 Organizing Committee, Osaka, Japan, pp. 922-931, 26th International Conference on Computational Linguistics, Osaka, Japan, 11/12/16. <https://www.aclweb.org/anthology/C16-1088>

TY - GEN

T1 - A Neural Model for Part-of-Speech Tagging in Historical Texts

AU - Hardmeier, Christian

PY - 2016/12/16

Y1 - 2016/12/16

N2 - 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.

AB - 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.

M3 - Conference contribution

SP - 922

EP - 931

BT - Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

PB - The COLING 2016 Organizing Committee

CY - Osaka, Japan

T2 - 26th International Conference on Computational Linguistics

Y2 - 11 December 2016 through 16 December 2016

ER -

A Neural Model for Part-of-Speech Tagging in Historical Texts

Abstract / Description of output

Conference

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