Native language identification with user generated content

Gili Goldin; Ella Rabinovich; Shuly Wintner

Native language identification with user generated content

Gili Goldin, Ella Rabinovich, Shuly Wintner

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Editors	Ellen Riloff, David Chiang, Julia Hockenmaier, Jun'ichi Tsujii
Publisher	Association for Computational Linguistics
Pages	3591-3601
Number of pages	11
ISBN (Electronic)	9781948087841
State	Published - 2020
Event	2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 - Brussels, Belgium Duration: 31 Oct 2018 → 4 Nov 2018

Publication series

Name	Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

Conference

Conference	2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Country/Territory	Belgium
City	Brussels
Period	31/10/18 → 4/11/18

Bibliographical 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:
© 2018 Association for Computational Linguistics

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Information Systems

Cite this

Goldin, G., Rabinovich, E., & Wintner, S. (2020). Native language identification with user generated content. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 (pp. 3591-3601). (Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018). Association for Computational Linguistics.

Goldin, Gili ; Rabinovich, Ella ; Wintner, Shuly. / Native language identification with user generated content. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018. editor / Ellen Riloff ; David Chiang ; Julia Hockenmaier ; Jun'ichi Tsujii. Association for Computational Linguistics, 2020. pp. 3591-3601 (Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018).

@inproceedings{7db5faff376846a2a82612ff88f2804e,

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 = "English",

series = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018",

publisher = "Association for Computational Linguistics",

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",

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Goldin, G, Rabinovich, E & Wintner, S 2020, Native language identification with user generated content. in E Riloff, D Chiang, J Hockenmaier & J Tsujii (eds), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, Association for Computational Linguistics, pp. 3591-3601, 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, Brussels, Belgium, 31/10/18.

Native language identification with user generated content. / Goldin, Gili; Rabinovich, Ella; Wintner, Shuly.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018. ed. / Ellen Riloff; David Chiang; Julia Hockenmaier; Jun'ichi Tsujii. Association for Computational Linguistics, 2020. p. 3591-3601 (Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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

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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.

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T3 - Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

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BT - Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

A2 - Riloff, Ellen

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Goldin G, Rabinovich E, Wintner S. Native language identification with user generated content. In Riloff E, Chiang D, Hockenmaier J, Tsujii J, editors, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018. Association for Computational Linguistics. 2020. p. 3591-3601. (Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018).

Native language identification with user generated content

Abstract

Publication series

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Bibliographical note

ASJC Scopus subject areas

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