Estimating Code-Switching on Twitter with a Novel Generalized Word-Level Language Detection Technique
Shruti Rijhwani, Royal Sequiera, Monojit Choudhury, Kalika Bali, Chandra Shekhar Maddila
Abstract
Word-level language detection is necessary for analyzing code-switched text, where multiple languages could be mixed within a sentence. Existing models are restricted to code-switching between two specific languages and fail in real-world scenarios as text input rarely has a priori information on the languages used. We present a novel unsupervised word-level language detection technique for code-switched text for an arbitrarily large number of languages, which does not require any manually annotated training data. Our experiments with tweets in seven languages show a 74% relative error reduction in word-level labeling with respect to competitive baselines. We then use this system to conduct a large-scale quantitative analysis of code-switching patterns on Twitter, both global as well as region-specific, with 58M tweets.- Anthology ID:
- P17-1180
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1971–1982
- Language:
- URL:
- https://aclanthology.org/P17-1180
- DOI:
- 10.18653/v1/P17-1180
- Cite (ACL):
- Shruti Rijhwani, Royal Sequiera, Monojit Choudhury, Kalika Bali, and Chandra Shekhar Maddila. 2017. Estimating Code-Switching on Twitter with a Novel Generalized Word-Level Language Detection Technique. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1971–1982, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Estimating Code-Switching on Twitter with a Novel Generalized Word-Level Language Detection Technique (Rijhwani et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/P17-1180.pdf