Poet Identification for Shahnameh of Ferdowsi using Artificial Neural Network
Abstract
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.
Keywords
Poet Identification, Author Identification, Text Categorization, Artificial Neural Network, Natural Language Processing, Artificial Intelligence, Shahnameh of Ferdowsi, Decision Tree System