• Nidhi Deepak
• Sara Mouzam
• Ramya Reddy

In a multilingual or socio-lingual configuration, Intra-sentential Code Switching (ICS) or Code Mixing (CM) is frequently observed nowadays. In the world most of the people know more than one language. The CM usage is especially apparent in social media platforms. Moreover, ICS is particularly significant in the context of technology, health and law where conveying the upcoming developments are difficult in one’s native language. In applications like dialog systems, machine translation, semantic parsing, shallow parsing, etc. CM and Code Switching pose serious challenges. To do any further advancement in code-mixed data, the necessary step is Language Identification. So, Here we present a study of Language Identification in English-Telugu Code Mixed Data. Considering the paucity of resources in code mixed languages, we proposed Logistic Regression where we compare the increase in Accuracy and F1- score before and after Feature Extraction. We have observed that first we got an Accuracy of 73.16% and after feature extraction we got an Accuracy of 89.77% and F1-Score of 0.8957.

NLP, Code-Switch, Code-Mix, Language Identification

"Language Identification & Analysis of Code- Switching Language", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.a575-a580, June-2022, Available :http://www.jetir.org/papers/JETIR2206067.pdf

"Language Identification & Analysis of Code- Switching Language", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppa575-a580, June-2022, Available at : http://www.jetir.org/papers/JETIR2206067.pdf