Abstract
Part-Of-Speech (POS) tagging is defined as the Natural Language Processing (NLP) task in which each word in a sentence is labeled with a tag indicating its appropriate part of speech. Of the entire supervised machine learning classification algorithms, second order Hidden Markov Model (HMM) and Conditional Random Fields (CRF) is chosen in this work for POS tagging of Kannada language. Training data includes 51,269 words and test data consists of around 2932 tokens. Both set being disjoint and taken from EMILLE corpus. Experiments show that the accuracy of the tools based on HMM and CRF is 79.9% and 84.58% respectively. General Terms Artificial Intelligence, Natural Language Processing, Machine learning.
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