Strategies for Language Identification in Code-Mixed Low Resource Languages
This addresses the problem of language tagging with limited data for code-mixed languages, representing an incremental improvement.
The paper tackles language identification in code-mixed low-resource languages by proposing three strategies for word-level tagging, achieving up to 91% accuracy individually and 92.6% with an ensemble system.
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%.