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Sprucing up the trees – error detection in treebanks

  • We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate our method in different scenarios: (i) for error detection in dependency treebanks and (ii) for improving parsing accuracy on in- and out-of-domain data.

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Metadaten
Author:Ines Rehbein, Josef Ruppenhofer
URN:urn:nbn:de:bsz:mh39-79938
URL:http://aclweb.org/anthology/C18-1010
ISBN:978-1-948087-50-6
Parent Title (English):Proceedings of the 27th International Conference on Computational Linguistics. August 20-26, 2018 Santa Fe, New Mexico, USA (COLING 2018)
Publisher:The Association for Computational Linguistics
Place of publication:Stroudsburg PA, USA
Editor:Emily M. Bender, Leon Derczynski, Pierre Isabelle
Document Type:Part of a Book
Language:English
Year of first Publication:2018
Date of Publication (online):2018/09/26
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
GND Keyword:Annotation; Automatische Spracherkennung; Parser
First Page:107
Last Page:118
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Leibniz-Classification:Sprache, Linguistik
Open Access?:Ja
Licence (English):License LogoCreative Commons - Attribution 4.0 International