A study of N-gram and Embedding Representations for Native Language Identification

Sowmya Vajjala, Sagnik Banerjee


Abstract
We report on our experiments with N-gram and embedding based feature representations for Native Language Identification (NLI) as a part of the NLI Shared Task 2017 (team name: NLI-ISU). Our best performing system on the test set for written essays had a macro F1 of 0.8264 and was based on word uni, bi and trigram features. We explored n-grams covering word, character, POS and word-POS mixed representations for this task. For embedding based feature representations, we employed both word and document embeddings. We had a relatively poor performance with all embedding representations compared to n-grams, which could be because of the fact that embeddings capture semantic similarities whereas L1 differences are more stylistic in nature.
Anthology ID:
W17-5026
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:
240–248
Language:
URL:
https://aclanthology.org/W17-5026
DOI:
10.18653/v1/W17-5026
Bibkey:
Cite (ACL):
Sowmya Vajjala and Sagnik Banerjee. 2017. A study of N-gram and Embedding Representations for Native Language Identification. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 240–248, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
A study of N-gram and Embedding Representations for Native Language Identification (Vajjala & Banerjee, BEA 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5026.pdf
Code
 nishkalavallabhi/NLIST2017
Data
italki NLI