@inproceedings{sarkar-etal-2020-non,
title = "The Non-native Speaker Aspect: {I}ndian {E}nglish in Social Media",
author = "Sarkar, Rupak and
Mahinder, Sayantan and
KhudaBukhsh, Ashiqur",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wnut-1.9/",
doi = "10.18653/v1/2020.wnut-1.9",
pages = "61--70",
abstract = "As the largest institutionalized second language variety of English, Indian English has received a sustained focus from linguists for decades. However, to the best of our knowledge, no prior study has contrasted web-expressions of Indian English in noisy social media with English generated by a social media user base that are predominantly native speakers. In this paper, we address this gap in the literature through conducting a comprehensive analysis considering multiple structural and semantic aspects. In addition, we propose a novel application of language models to perform automatic linguistic quality assessment."
}
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<abstract>As the largest institutionalized second language variety of English, Indian English has received a sustained focus from linguists for decades. However, to the best of our knowledge, no prior study has contrasted web-expressions of Indian English in noisy social media with English generated by a social media user base that are predominantly native speakers. In this paper, we address this gap in the literature through conducting a comprehensive analysis considering multiple structural and semantic aspects. In addition, we propose a novel application of language models to perform automatic linguistic quality assessment.</abstract>
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%0 Conference Proceedings
%T The Non-native Speaker Aspect: Indian English in Social Media
%A Sarkar, Rupak
%A Mahinder, Sayantan
%A KhudaBukhsh, Ashiqur
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sarkar-etal-2020-non
%X As the largest institutionalized second language variety of English, Indian English has received a sustained focus from linguists for decades. However, to the best of our knowledge, no prior study has contrasted web-expressions of Indian English in noisy social media with English generated by a social media user base that are predominantly native speakers. In this paper, we address this gap in the literature through conducting a comprehensive analysis considering multiple structural and semantic aspects. In addition, we propose a novel application of language models to perform automatic linguistic quality assessment.
%R 10.18653/v1/2020.wnut-1.9
%U https://aclanthology.org/2020.wnut-1.9/
%U https://doi.org/10.18653/v1/2020.wnut-1.9
%P 61-70
Markdown (Informal)
[The Non-native Speaker Aspect: Indian English in Social Media](https://aclanthology.org/2020.wnut-1.9/) (Sarkar et al., WNUT 2020)
ACL