Publication Type : Conference Paper
Publisher : 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Bangalore, India.
Source : 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Bangalore, India (2018)
Url : https://ieeexplore.ieee.org/abstract/document/8554413
Keywords : Adaboost, Code mixed, code mixing, code-mixed messages, Data mining, Feature extraction, Feature vectors, Gaussian Naive Bayes, kNN, Language identification, linguistic proficiency, logistic regression, Named entity recognition, NAtural language processing, NER, part-of-speech tagging, polarity identification, Question Answering, question answering (information retrieval), Random forest, randomly merged English words, Social media platforms, Social networking (online), Support vector machines, SVM, Switches, Tagging, Task analysis, text analysis, Text mining, Transliteration, Twitter .
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Center : Computational Linguistics and Indic Studies
Department : Computer Science
Verified : No
Year : 2018
Abstract : Indians and many other non-English speakers across the world, prefer not to use single code in their messaging texts on social media platforms. They make use of transliteration and randomly merged English words using code-mixing, two or more languages to show their linguistic proficiency (English-Spanish, Arabic-English, etc.). Code-mixing (CM) is a dynamically progressive area of research in the domain of text mining. Present time communications in social media, blogs, reviews are abuzz with creative, crafty code-mixed messages. This paper highlights a comprehensive study of CM in the diverse fields of Natural Language Processing (NLP) including language identification, Part-of-Speech (POS) tagging, Named Entity Recognition (NER), Polarity Identification, Question Answering. CM has also been sought after in studies involving Machine Translation, Dialect identification, Speech technologies etc. Most of the applications of code mixing are scrutinized and presented briefly in this survey. This study purports to articulate tends and, techniques pursued in languages used and also unique evaluation measures to give accuracy.
Cite this Research Publication : S. Thara and Poornachandran, P., “Code-Mixing: A Brief Survey”, in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, 2018