The CLIN27 Shared Task: Translating Historical Text to Contemporary Language for Improving Automatic Linguistic Annotation

E. Tjong Kim Sang; Marcel Bollman; Remko Boschker; Francisco Casacuberta; Feike Dietz; Stefanie Dipper; Miguel Domingo; Rob van der Goot; Marjo van Koppen; Nikola Ljubesic; Robert Ostling; Florian Petran; Eva Pettersson; Yves Scherrer; Marijn Schraagen; Leen Sevens; Jorg Tiedemann; Tom Vanallemeersch; Kalliopi Zervanou

doi:10.1109/eScience.2017.60

The CLIN27 Shared Task: Translating Historical Text to Contemporary Language for Improving Automatic Linguistic Annotation

E. Tjong Kim Sang, Marcel Bollman, Remko Boschker, Francisco Casacuberta, Feike Dietz, Stefanie Dipper, Miguel Domingo, Rob van der Goot, Marjo van Koppen, Nikola Ljubesic, Robert Ostling, Florian Petran, Eva Pettersson, Yves Scherrer, Marijn Schraagen, Leen Sevens, Jorg Tiedemann, Tom Vanallemeersch, Kalliopi Zervanou

Meertens Institute

Research output: Contribution to journal/periodical › Article › Scientific › peer-review

Abstract

The CLIN27 shared task evaluates the effect of translating historical text to modern text with the goal of improving the quality of the output of contemporary natural language processing tools applied to the text. We focus on improving part-of-speech tagging analysis of seventeenth-century Dutch. Eight teams took part in the shared task. The best results were obtained by teams employing character-based machine translation. The best system obtained an error reduction of 51% in comparison with the baseline of tagging unmodified text. This is close to the error reduction obtained by human translation (57%).

Original language	English
Article number	88
Pages (from-to)	53-64
Number of pages	12
Journal	Computational Linguistics in the Netherlands Journal
Volume	7
DOIs	https://doi.org/10.1109/eScience.2017.60
Publication status	Published - 2017

Keywords

historical text
Dutch
machine translation
part-of-speech tagging

Access to Document

10.1109/eScience.2017.60

Cite this

Tjong Kim Sang, E., Bollman, M., Boschker, R., Casacuberta, F., Dietz, F., Dipper, S., Domingo, M., van der Goot, R., van Koppen, M., Ljubesic, N., Ostling, R., Petran, F., Pettersson, E., Scherrer, Y., Schraagen, M., Sevens, L., Tiedemann, J., Vanallemeersch, T., & Zervanou, K. (2017). The CLIN27 Shared Task: Translating Historical Text to Contemporary Language for Improving Automatic Linguistic Annotation. Computational Linguistics in the Netherlands Journal, 7, 53-64. [88]. https://doi.org/10.1109/eScience.2017.60

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title = "The CLIN27 Shared Task: Translating Historical Text to Contemporary Language for Improving Automatic Linguistic Annotation",

abstract = "The CLIN27 shared task evaluates the effect of translating historical text to modern text with the goal of improving the quality of the output of contemporary natural language processing tools applied to the text. We focus on improving part-of-speech tagging analysis of seventeenth-century Dutch. Eight teams took part in the shared task. The best results were obtained by teams employing character-based machine translation. The best system obtained an error reduction of 51% in comparison with the baseline of tagging unmodified text. This is close to the error reduction obtained by human translation (57%).",

keywords = "historical text, Dutch, machine translation , part-of-speech tagging",

author = "{Tjong Kim Sang}, E. and Marcel Bollman and Remko Boschker and Francisco Casacuberta and Feike Dietz and Stefanie Dipper and Miguel Domingo and {van der Goot}, Rob and {van Koppen}, Marjo and Nikola Ljubesic and Robert Ostling and Florian Petran and Eva Pettersson and Yves Scherrer and Marijn Schraagen and Leen Sevens and Jorg Tiedemann and Tom Vanallemeersch and Kalliopi Zervanou",

year = "2017",

doi = "10.1109/eScience.2017.60",

language = "English",

volume = "7",

pages = "53--64",

journal = "Computational Linguistics in the Netherlands Journal",

issn = "2211-4009",

publisher = "CLIN",

}

Tjong Kim Sang, E, Bollman, M, Boschker, R, Casacuberta, F, Dietz, F, Dipper, S, Domingo, M, van der Goot, R, van Koppen, M, Ljubesic, N, Ostling, R, Petran, F, Pettersson, E, Scherrer, Y, Schraagen, M, Sevens, L, Tiedemann, J, Vanallemeersch, T & Zervanou, K 2017, 'The CLIN27 Shared Task: Translating Historical Text to Contemporary Language for Improving Automatic Linguistic Annotation', Computational Linguistics in the Netherlands Journal, vol. 7, 88, pp. 53-64. https://doi.org/10.1109/eScience.2017.60

TY - JOUR

T1 - The CLIN27 Shared Task: Translating Historical Text to Contemporary Language for Improving Automatic Linguistic Annotation

AU - Tjong Kim Sang, E.

AU - Bollman, Marcel

AU - Boschker, Remko

AU - Casacuberta, Francisco

AU - Dietz, Feike

AU - Dipper, Stefanie

AU - Domingo, Miguel

AU - van der Goot, Rob

AU - van Koppen, Marjo

AU - Ljubesic, Nikola

AU - Ostling, Robert

AU - Petran, Florian

AU - Pettersson, Eva

AU - Scherrer, Yves

AU - Schraagen, Marijn

AU - Sevens, Leen

AU - Tiedemann, Jorg

AU - Vanallemeersch, Tom

AU - Zervanou, Kalliopi

PY - 2017

Y1 - 2017

N2 - The CLIN27 shared task evaluates the effect of translating historical text to modern text with the goal of improving the quality of the output of contemporary natural language processing tools applied to the text. We focus on improving part-of-speech tagging analysis of seventeenth-century Dutch. Eight teams took part in the shared task. The best results were obtained by teams employing character-based machine translation. The best system obtained an error reduction of 51% in comparison with the baseline of tagging unmodified text. This is close to the error reduction obtained by human translation (57%).

AB - The CLIN27 shared task evaluates the effect of translating historical text to modern text with the goal of improving the quality of the output of contemporary natural language processing tools applied to the text. We focus on improving part-of-speech tagging analysis of seventeenth-century Dutch. Eight teams took part in the shared task. The best results were obtained by teams employing character-based machine translation. The best system obtained an error reduction of 51% in comparison with the baseline of tagging unmodified text. This is close to the error reduction obtained by human translation (57%).

KW - historical text

KW - Dutch

KW - machine translation

KW - part-of-speech tagging

UR - https://ifarm.nl/clin2017st/

U2 - 10.1109/eScience.2017.60

DO - 10.1109/eScience.2017.60

M3 - Article

SN - 2211-4009

VL - 7

SP - 53

EP - 64

JO - Computational Linguistics in the Netherlands Journal

JF - Computational Linguistics in the Netherlands Journal

M1 - 88

ER -

The CLIN27 Shared Task: Translating Historical Text to Contemporary Language for Improving Automatic Linguistic Annotation

Abstract

Keywords

Access to Document

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Fingerprint

Improving Part-of-Speech Tagging of Historical Text by First Translating to Modern Text

Converting Seventeenth-Century Dutch to Modern Dutch

Cite this

The CLIN27 Shared Task: Translating Historical Text to Contemporary Language for Improving Automatic Linguistic Annotation

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Research output

Improving Part-of-Speech Tagging of Historical Text by First Translating to Modern Text

Activities

Converting Seventeenth-Century Dutch to Modern Dutch

Cite this