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arXiv · 1810.07156

Strategies for Language Identification in Code-Mixed Low Resource Languages

Soumil Mandal·Sankalp Sanand
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Abstract

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%.

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Soumil Mandal, Sankalp Sanand. 2018-10-16. Strategies for Language Identification in Code-Mixed Low Resource Languages. https://arxiv.org/abs/1810.07156

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