Abstract
We present a language-independent treebank annotation tool supporting rich annotations with discontinuous constituents and function tags. Candidate analyses are generated by an exemplar-based parsing model that immediately learns from each new annotated sentence during annotation. This makes it suitable for situations in which only a limited seed treebank is available, or a radically different domain is being annotated. The tool offers the possibility to experiment with and evaluate active learning methods to speed up annotation in a naturalistic setting, i.e., measuring actual annotation costs and tracking specific user interactions. The code is made available under the GNU GPL license at https://github.com/andreasvc/activedop.- Anthology ID:
- C18-2009
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico
- Editor:
- Dongyan Zhao
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 38–42
- Language:
- URL:
- https://aclanthology.org/C18-2009
- DOI:
- Cite (ACL):
- Andreas van Cranenburgh. 2018. Active DOP: A constituency treebank annotation tool with online learning. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 38–42, Santa Fe, New Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Active DOP: A constituency treebank annotation tool with online learning (van Cranenburgh, COLING 2018)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/C18-2009.pdf
- Code
- andreasvc/activedop
- Data
- Penn Treebank