  Use of social media has grown dramatically during the last few years. Users
follow informal languages in communicating through social media. The language
of communication is often mixed in nature, where people transcribe their
regional language with English and this technique is found to be extremely
popular. Natural language processing (NLP) aims to infer the information from
these text where Part-of-Speech (PoS) tagging plays an important role in
getting the prosody of the written text. For the task of PoS tagging on
Code-Mixed Indian Social Media Text, we develop a supervised system based on
Conditional Random Field classifier. In order to tackle the problem
effectively, we have focused on extracting rich linguistic features. We
participate in three different language pairs, ie. English-Hindi,
English-Bengali and English-Telugu on three different social media platforms,
Twitter, Facebook & WhatsApp. The proposed system is able to successfully
assign coarse as well as fine-grained PoS tag labels for a given a code-mixed
sentence. Experiments show that our system is quite generic that shows
encouraging performance levels on all the three language pairs in all the
domains.
