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.- Anthology ID:
- 2020.wnut-1.9
- Volume:
- Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 61–70
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.9
- DOI:
- 10.18653/v1/2020.wnut-1.9
- Cite (ACL):
- Rupak Sarkar, Sayantan Mahinder, and Ashiqur KhudaBukhsh. 2020. The Non-native Speaker Aspect: Indian English in Social Media. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 61–70, Online. Association for Computational Linguistics.
- Cite (Informal):
- The Non-native Speaker Aspect: Indian English in Social Media (Sarkar et al., WNUT 2020)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2020.wnut-1.9.pdf