Language Identification of Bengali-English Code-Mixed data using Character & Phonetic based LSTM Models
This addresses the challenge of language identification in low-resource code-mixed data for social media applications, representing an incremental improvement.
The paper tackled language identification for Bengali-English code-mixed social media text by developing LSTM models with character and phonetic encodings, achieving accuracies of 91.78% and 92.35% using ensemble methods.
Language identification of social media text still remains a challenging task due to properties like code-mixing and inconsistent phonetic transliterations. In this paper, we present a supervised learning approach for language identification at the word level of low resource Bengali-English code-mixed data taken from social media. We employ two methods of word encoding, namely character based and root phone based to train our deep LSTM models. Utilizing these two models we created two ensemble models using stacking and threshold technique which gave 91.78% and 92.35% accuracies respectively on our testing data.