A Novel Approach for Odia Part of Speech Tagging Using Artificial Neural Network

This paper presents a challenging task for POS Tagging using Artificial Neural Network for Odia language. Neural Network is used for Odia POS Tagging. A Single Neural Network based POS Tagger with fixed length of context chosen empirically is presented first. Then a multiple neuron tagger which consists of multiple single-neuron taggers with fixed but different lengths of contexts is presented. Multi-neuron tagger performs tagging by voting on the output of all single neuron tagger. The experiments carried out are discussed, Neural Network for efficient recognition where the errors were corrected through forward propagation and rectified neuron values were transmitted by feed-forward method in the neural network of multiple layers, i.e. the input layer, the output layer and the middle layer or hidden layers. Neural networks are one of the most efficient techniques for identified the correct data. A small labeled training set is provided; a HMM based approach does not yield very good result. So in this work, morphological analyzer is used to improve the performance of the tagger. This tagger has an accuracy of about 81% on the test data provided.