Part of Speech Tagging in Odia Using Support Vector Machine

Part of Speech (POS) Tagging is a challenging task to identify the meaning of each word in a sentence. This paper shows the task of identifying each word in an odia sentence using the technique of Support Vector Machine. The POS Tagger is developed using a very small tagset of five tags. Various features sets are taken for different contextual information is helpful in predicting the POS classes. An Odia corpus of 10,000 words has taken and tested it very carefully. The previous POS Tagger was done using Artificial Neural Network (ANN) had given the accuracy of 81%. But this SVM based POS Tagger for Odia gives the result with an accuracy of 82%. It is very helpful to use in many field of natural language process. The result of this system compares with POS tagger using ANN which was previously done.