Neural Computation in Authorship Attribution: The Case of Selected Tamil Articles∗

Abstract

Neural networks regard author attribution as a problem of pattern recognition and the proven results of their applications make them promising techniques for the future. Several neural networks are being applied for authorship determination. Learning vector quantization (LVQ) is a neural network technique that develops a codebook of quantization vectors and makes use of these vectors to encode any input vector. In this article an attempt is made to attribute authorship to disputed articles using LVQ and verify them with the results obtained by traditional canonical discriminant analysis. This study demonstrates that statistical methods of attributing authorship can be paired effectively with neural networks to produce a powerful classification tool. Comparisons are made using means of 24 function words identified from the 32 articles written in the Tamil language by three contemporary scholars of great repute to determine the authorship of 23 unattributed articles pertaining to the same period. This study establishes the fact that LVQ is a powerful technique for computational stylistics.