@inproceedings{442aba7d889f48deb145f1263d8cb814,
title = "Native language identification with user generated content",
abstract = "We address the task of native language identification in the context of social media content, where authors are highly-fluent, advanced nonnative speakers (of English). Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task. We provide a detailed analysis of the features that sheds light on differences between native and nonnative speakers, and among nonnative speakers with different backgrounds.",
author = "Gili Goldin and Ella Rabinovich and Shuly Wintner",
note = "Funding Information: This research was supported by Grant No. 2017699 from the United States-Israel Binational Science Foundation (BSF) and by Grant No. 1813153 from the United States National Science Foundation (NSF). Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics; 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 ; Conference date: 31-10-2018 Through 04-11-2018",
year = "2020",
language = "American English",
series = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018",
pages = "3591--3601",
editor = "Ellen Riloff and David Chiang and Julia Hockenmaier and Jun'ichi Tsujii",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018",
}