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Building Part-of-Speech (POS) taggers for code-mixed Indian languages is aparticularly challenging problem in computational linguistics due to a dearthof accurately annotated training corpora. ICON, as part of its NLP toolscontest has organized this challenge as a shared task for the secondconsecutive year to improve the state-of-the-art. This paper describes the POStagger built at Surukam to predict the coarse-grained and fine-grained POS tagsfor 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 sequencetagging algorithm and used a library called sklearn-crfsuite - a thin wrapperaround 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 constrainedenvironment,i.e., without making any use of monolingual POS taggers or thelike, obtained an overall average F1-score of 76.45%, which is comparable tothe 2015 winning score of 76.79%.

A POS Tagger for Code Mixed Indian Social Media Text - ICON-2016 NLP Tools Contest Entry from Surukam