Poet Identification for Shahnameh of Ferdowsi using Artificial Neural Network

Poet identification is a major branch of author identification, which is one important issue in text categorization and natural language processing. This paper discuses intelligent recognition of Shahnameh of Ferdowsi that uses feed-forward back-propagation (FF/BP) artificial neural networks (ANN). We also experiment the results of a decision tree system (DTS) for comparison. In poetic feature extraction phase, we try to simulate the human thinking about poem understanding and recognition. So we extract more than 64 data inputs from 22 various features which are categorized in physical, conceptual, and rhythmic classes. We have designed and implemented a special database of Persian words for quantitative measurement and examination of samples. According to the experimental results the DTS method achieved more than 85% of accuracy on classifying poems into the Shahnameh and non-Shahnameh classes, whereas ANN accomplished 100%. Afterward, to make an automatic feature reduction and determining usefulness of every poetic feature, we used a single Perceptron network. Combination of this network with the above methods keeps accuracy of classifiers, but decreases loadings of FF/BP-ANN.