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Character embedding for language identification in Hindi-English code-mixed social media text

Publication Type : Journal Article

Publisher : Computacion y Sistemas

Source : Computacion y Sistemas, Instituto Politecnico Nacional, Volume 22, Number 1, p.65-74 (2018)

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Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Computational Neuroscience Laboratory, Computer Science, Electronics and Communication

Verified : No

Year : 2018

Abstract : Social media platforms are now widely used by the people to express their opinion or interest. The language used by the users in social media earlier was purely English. Code-mixed text, i.e., mixing of two or more languages, is commonly seen now. In code-mixed data, one language will be written using another language script. So to process such code-mixed text, identification of language used in each word is important for language processing. The main objective of the work is to propose a technique for identifying the language of Hindi-English code-mixed data used in three social media platforms namely, Facebook, Twitter, and WhatsApp. The classification of Hindi-English code-mixed data into Hindi, English, Named Entity, Acronym, Universal, Mixed (Hindi along with English) and Undefined tags were performed. Popular word embedding features were used for the representation of each word. Two kinds of embedding features were considered - word-based embedding features and character-based context features. The proposed method was done with the addition of context information along with the embedding features. A well-known machine learning classifier, Support Vector Machine was used to train and test the system. The work on Language Identification in code-mixed text using character-based embedding is a novel approach and shows promising results. © 2018 Instituto Politecnico Nacional. All rights reserved.

Cite this Research Publication : P. V. Veena, Dr. M. Anand Kumar, and Dr. Soman K. P., “Character embedding for language identification in Hindi-English code-mixed social media text”, Computacion y Sistemas, vol. 22, pp. 65-74, 2018.

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