TY - GEN
T1 - Native language identification with user generated content
AU - Goldin, Gili
AU - Rabinovich, Ella
AU - Wintner, Shuly
N1 - 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: © 2018 Association for Computational Linguistics
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85077694612
M3 - Conference contribution
T3 - Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
SP - 3591
EP - 3601
BT - Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
A2 - Riloff, Ellen
A2 - Chiang, David
A2 - Hockenmaier, Julia
A2 - Tsujii, Jun'ichi
T2 - 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Y2 - 31 October 2018 through 4 November 2018
ER -