  Social media platforms such as Twitter and Facebook are becoming popular in
multilingual societies. This trend induces portmanteau of South Asian languages
with English. The blend of multiple languages as code-mixed data has recently
become popular in research communities for various NLP tasks. Code-mixed data
consist of anomalies such as grammatical errors and spelling variations. In
this paper, we leverage the contextual property of words where the different
spelling variation of words share similar context in a large noisy social media
text. We capture different variations of words belonging to same context in an
unsupervised manner using distributed representations of words. Our experiments
reveal that preprocessing of the code-mixed dataset based on our approach
improves the performance in state-of-the-art part-of-speech tagging
(POS-tagging) and sentiment analysis tasks.
