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An accurate language identification tool is an absolute necessity forbuilding complex NLP systems to be used on code-mixed data. Lot of work hasbeen recently done on the same, but there's still room for improvement.Inspired from the recent advancements in neural network architectures forcomputer vision tasks, we have implemented multichannel neural networkscombining CNN and LSTM for word level language identification of code-mixeddata. Combining this with a Bi-LSTM-CRF context capture module, accuracies of93.28% and 93.32% is achieved on our two testing sets.

Language Identification in Code-Mixed Data using Multichannel Neural Networks and Context Capture