Improved word vector space with ensemble deep learning model for language identification

The process of determining native tongue of document is known as language identification. This work presents word level language identification of text as English or Hindi. Experimental analysis is performed on dataset collected from Twitter. In the first step, collected data is preprocessed by applying natural language processing techniques. Ensemble word embedding technique is proposed by ensembling four word embedding techniques namely, (i) Word2Vec, (ii) Embeddings from Language Model, (iii) Global Vectors, and (iv) FastText. Proposed word embedding approach is applied on preprocessed data to get enhanced word vector space for language identification. Finally, classification of text as Hindi or English is performed by four heterogeneous deep learning models namely, (i) Convolution Neural Network (CNN), (ii) Long Short Term Memory (LSTM), (iii) Hybrid model of CNN and LSTM, and (iv) Hybrid model of Bidirectional Long Short-Term Memory and Gated Recurrent Unit. Proposed hybrid model gives highest 96.05%, 95.13%, 94.21%, and 97.67% precision, F-score, sensitivity, and accuracy, respectively. Outcome obtained by the proposed model is higher as compare to single deep learning approach.