@inproceedings{zampieri-etal-2017-native,
title = "Native Language Identification on Text and Speech",
author = "Zampieri, Marcos and
Ciobanu, Alina Maria and
Dinu, Liviu P.",
editor = "Tetreault, Joel and
Burstein, Jill and
Leacock, Claudia and
Yannakoudakis, Helen",
booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5045",
doi = "10.18653/v1/W17-5045",
pages = "398--404",
abstract = "This paper presents an ensemble system combining the output of multiple SVM classifiers to native language identification (NLI). The system was submitted to the NLI Shared Task 2017 fusion track which featured students essays and spoken responses in form of audio transcriptions and iVectors by non-native English speakers of eleven native languages. Our system competed in the challenge under the team name ZCD and was based on an ensemble of SVM classifiers trained on character n-grams achieving 83.58{\%} accuracy and ranking 3rd in the shared task.",
}
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%0 Conference Proceedings
%T Native Language Identification on Text and Speech
%A Zampieri, Marcos
%A Ciobanu, Alina Maria
%A Dinu, Liviu P.
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F zampieri-etal-2017-native
%X This paper presents an ensemble system combining the output of multiple SVM classifiers to native language identification (NLI). The system was submitted to the NLI Shared Task 2017 fusion track which featured students essays and spoken responses in form of audio transcriptions and iVectors by non-native English speakers of eleven native languages. Our system competed in the challenge under the team name ZCD and was based on an ensemble of SVM classifiers trained on character n-grams achieving 83.58% accuracy and ranking 3rd in the shared task.
%R 10.18653/v1/W17-5045
%U https://aclanthology.org/W17-5045
%U https://doi.org/10.18653/v1/W17-5045
%P 398-404
Markdown (Informal)
[Native Language Identification on Text and Speech](https://aclanthology.org/W17-5045) (Zampieri et al., BEA 2017)
ACL
- Marcos Zampieri, Alina Maria Ciobanu, and Liviu P. Dinu. 2017. Native Language Identification on Text and Speech. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 398–404, Copenhagen, Denmark. Association for Computational Linguistics.