Sprucing up the trees – Error detection in treebanks

Ines Rehbein, Josef Ruppenhofer


Abstract
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.
Anthology ID:
C18-1010
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
107–118
Language:
URL:
https://www.aclweb.org/anthology/C18-1010
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C18-1010.pdf