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
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 107–118
- Language:
- URL:
- https://aclanthology.org/C18-1010
- DOI:
- Cite (ACL):
- Ines Rehbein and Josef Ruppenhofer. 2018. Sprucing up the trees – Error detection in treebanks. In Proceedings of the 27th International Conference on Computational Linguistics, pages 107–118, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Sprucing up the trees – Error detection in treebanks (Rehbein & Ruppenhofer, COLING 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/C18-1010.pdf
- Data
- English Web Treebank, Universal Dependencies