# Prevalence of code mixing in semi-formal patient communication in low resource languages of South Africa

arXiv: https://arxiv.org/abs/1911.05636 · HTML: https://mlsanity.com/paper/1911.05636/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns](https://mlsanity.com/paper/2211.07628/) (2022)

## Abstract (arXiv)

In this paper we address the problem of code-mixing in resource-poor language settings. We examine data consisting of 182k unique questions generated by users of the MomConnect helpdesk, part of a national scale public health platform in South Africa. We show evidence of code-switching at the level of approximately 10% within this dataset -- a level that is likely to pose challenges for future services. We use a natural language processing library (Polyglot) that supports detection of 196 languages and attempt to evaluate its performance at identifying English, isiZulu and code-mixed questions.