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

arXiv: https://arxiv.org/abs/1808.07118 · HTML: https://mlsanity.com/paper/1808.07118/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis](https://mlsanity.com/paper/2408.08964/) (2024)
- [Offensive Language Identification in Low-resourced Code-mixed Dravidian languages using Pseudo-labeling](https://mlsanity.com/paper/2108.12177/) (2021)
- [CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Texts](https://mlsanity.com/paper/2211.09847/) (2022)
- [Automatic Language Identification for Celtic Texts](https://mlsanity.com/paper/2203.04831/) (2022)
- [IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing](https://mlsanity.com/paper/2609.11851/) (2026)

## Abstract (arXiv)

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.