  POS Tagging serves as a preliminary task for many NLP applications. Kannada
is a relatively poor Indian language with very limited number of quality NLP
tools available for use. An accurate and reliable POS Tagger is essential for
many NLP tasks like shallow parsing, dependency parsing, sentiment analysis,
named entity recognition. We present a statistical POS tagger for Kannada using
different machine learning and neural network models. Our Kannada POS tagger
outperforms the state-of-the-art Kannada POS tagger by 6%. Our contribution in
this paper is three folds - building a generic POS Tagger, comparing the
performances of different modeling techniques, exploring the use of character
and word embeddings together for Kannada POS Tagging.
