  In recent years, substantial work has been done on language tagging of
code-mixed data, but most of them use large amounts of data to build their
models. In this article, we present three strategies to build a word level
language tagger for code-mixed data using very low resources. Each of them
secured an accuracy higher than our baseline model, and the best performing
system got an accuracy around 91%. Combining all, the ensemble system achieved
an accuracy of around 92.6%.
