Title
Language Identification of Bengali-English Code-Mixed Data using Character & Phonetic based LSTM Models
Abstract
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.
Year
DOI
Venue
2019
10.1145/3368567.3368578
Proceedings of the 11th Forum for Information Retrieval Evaluation
Keywords
Field
DocType
character encoding, code-mixing, code-switching, language identification, phonetic encoding
Code-switching,Computer science,Supervised learning,Phone,Bengali,Test data,Artificial intelligence,Language identification,Natural language processing,Character encoding,Code-mixing
Conference
ISBN
Citations 
PageRank 
978-1-4503-7750-8
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Sourya Dipta Das174.47
Soumil Mandal200.34
D. Das371776.14