Authorship Attribution on Kannada Text using Bi-Directional LSTM Technique

Abstract

Author attribution is the field of deducing the author of an unknown textual source based on certain characteristics inherently present in the author’s style of writing. Author attribution has a ton of useful applications which help automate manual tasks. The proposed model is designed to predict the authorship of the Kannada text using a sequential neural network with Bi-Directional Long Short Term Memory layers, Dense layers, Activation function and Dropout layers. Based on the nature of the data, we have used stochastic gradient descent as an optimizer that improves the learning of the proposed model. The model extracts Part of the speech tags as one of the semantic features using the N-gram technique. A Conditional random fields model is developed to assign Part of the speech tags for the Kannada text tokens, which is the base for the proposed model. The parts of the speech model achieve an overall 90% and 91% F1 score and accuracy respectively. There is no state-of-art model to compare the performance of our model with other models developed for the Kannada language. The proposed model is evaluated using the One Versus Five (1 vs 5) method and overall accuracy of 77.8% is achieved.