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

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 transla... Mehr ...

Volltext
Verfasser: Tjong Kim Sang, E.
Bollman, Marcel
Boschker, Remko
Casacuberta, Francisco
Dietz, Feike
Dipper, Stefanie
Domingo, Miguel
van der Goot, Rob
van Koppen, Marjo
Ljubesic, Nikola
Ostling, Robert
Petran, Florian
Pettersson, Eva
Scherrer, Yves
Schraagen, Marijn
Sevens, Leen
Tiedemann, Jorg
Vanallemeersch, Tom
Zervanou, Kalliopi
Dokumenttyp: Artikel
Erscheinungsdatum: 2017
Reihe/Periodikum: 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
Schlagwörter: historical text / Dutch / machine translation / part-of-speech tagging
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-28995688
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://pure.knaw.nl/portal/en/publications/ed831e17-66b0-4a54-bf6b-d9d164bb12ae

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%).