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%.
@article{arxiv.1810.07156,
title = {Strategies for Language Identification in Code-Mixed Low Resource Languages},
author = {Soumil Mandal and Sankalp Sanand},
journal= {arXiv preprint arXiv:1810.07156},
year = {2018}
}
Comments
International Conference on Natural Language Processing (ICON 18) - Student Paper Competition, Patiala, India