  Building Part-of-Speech (POS) taggers for code-mixed Indian languages is a
particularly challenging problem in computational linguistics due to a dearth
of accurately annotated training corpora. ICON, as part of its NLP tools
contest has organized this challenge as a shared task for the second
consecutive year to improve the state-of-the-art. This paper describes the POS
tagger built at Surukam to predict the coarse-grained and fine-grained POS tags
for three language pairs - Bengali-English, Telugu-English and Hindi-English,
with the text spanning three popular social media platforms - Facebook,
WhatsApp and Twitter. We employed Conditional Random Fields as the sequence
tagging algorithm and used a library called sklearn-crfsuite - a thin wrapper
around CRFsuite for training our model. Among the features we used include -
character n-grams, language information and patterns for emoji, number,
punctuation and web-address. Our submissions in the constrained
environment,i.e., without making any use of monolingual POS taggers or the
like, obtained an overall average F1-score of 76.45%, which is comparable to
the 2015 winning score of 76.79%.
