How Bad are PoS Tagger in Cross-Corpora Settings? Evaluating Annotation Divergence in the UD Project.

Guillaume Wisniewski, François Yvon

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Abstract
The performance of Part-of-Speech tagging varies significantly across the treebanks of the Universal Dependencies project. This work points out that these variations may result from divergences between the annotation of train and test sets. We show how the annotation variation principle, introduced by Dickinson and Meurers (2003) to automatically detect errors in gold standard, can be used to identify inconsistencies between annotations; we also evaluate their impact on prediction performance.
Anthology ID:
N19-1019
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
218–227
Language:
URL:
https://aclanthology.org/N19-1019
DOI:
10.18653/v1/N19-1019
Bibkey:
Cite (ACL):
Guillaume Wisniewski and François Yvon. 2019. How Bad are PoS Tagger in Cross-Corpora Settings? Evaluating Annotation Divergence in the UD Project.. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 218–227, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
How Bad are PoS Tagger in Cross-Corpora Settings? Evaluating Annotation Divergence in the UD Project. (Wisniewski & Yvon, NAACL 2019)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/N19-1019.pdf
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