The Power of Character N-grams in Native Language Identification
Artur Kulmizev, Bo Blankers, Johannes Bjerva, Malvina Nissim, Gertjan van Noord, Barbara Plank, Martijn Wieling
Abstract
In this paper, we explore the performance of a linear SVM trained on language independent character features for the NLI Shared Task 2017. Our basic system (GRONINGEN) achieves the best performance (87.56 F1-score) on the evaluation set using only 1-9 character n-grams as features. We compare this against several ensemble and meta-classifiers in order to examine how the linear system fares when combined with other, especially non-linear classifiers. Special emphasis is placed on the topic bias that exists by virtue of the assessment essay prompt distribution.- Anthology ID:
- W17-5043
- 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:
- 382–389
- Language:
- URL:
- https://aclanthology.org/W17-5043
- DOI:
- 10.18653/v1/W17-5043
- Cite (ACL):
- Artur Kulmizev, Bo Blankers, Johannes Bjerva, Malvina Nissim, Gertjan van Noord, Barbara Plank, and Martijn Wieling. 2017. The Power of Character N-grams in Native Language Identification. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 382–389, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- The Power of Character N-grams in Native Language Identification (Kulmizev et al., BEA 2017)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W17-5043.pdf
- Data
- Universal Dependencies