Neural Networks and Spelling Features for Native Language Identification
Johannes Bjerva, Gintarė Grigonytė, Robert Östling, Barbara Plank
Abstract
We present the RUG-SU team’s submission at the Native Language Identification Shared Task 2017. We combine several approaches into an ensemble, based on spelling error features, a simple neural network using word representations, a deep residual network using word and character features, and a system based on a recurrent neural network. Our best system is an ensemble of neural networks, reaching an F1 score of 0.8323. Although our system is not the highest ranking one, we do outperform the baseline by far.- Anthology ID:
- W17-5025
- Volume:
- Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Joel Tetreault, Jill Burstein, Claudia Leacock, Helen Yannakoudakis
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 235–239
- Language:
- URL:
- https://aclanthology.org/W17-5025
- DOI:
- 10.18653/v1/W17-5025
- Cite (ACL):
- Johannes Bjerva, Gintarė Grigonytė, Robert Östling, and Barbara Plank. 2017. Neural Networks and Spelling Features for Native Language Identification. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 235–239, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Neural Networks and Spelling Features for Native Language Identification (Bjerva et al., BEA 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W17-5025.pdf