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

TL;DR

This paper introduces a multichannel neural network approach combining CNN, LSTM, and Bi-LSTM-CRF to improve language identification accuracy in code-mixed data, achieving over 93% accuracy.

Contribution

It presents a novel neural network architecture that integrates CNN, LSTM, and context capture modules specifically for code-mixed language identification.

Findings

Achieved over 93% accuracy on test datasets.

Demonstrated effectiveness of multichannel neural networks for language ID.

Enhanced context understanding improves identification performance.

Abstract

An accurate language identification tool is an absolute necessity for building complex NLP systems to be used on code-mixed data. Lot of work has been recently done on the same, but there's still room for improvement. Inspired from the recent advancements in neural network architectures for computer vision tasks, we have implemented multichannel neural networks combining CNN and LSTM for word level language identification of code-mixed data. Combining this with a Bi-LSTM-CRF context capture module, accuracies of 93.28% and 93.32% is achieved on our two testing sets.