NDLI: A comparative study on authorship attribution classification tasks using both neural network and statistical methods

Content Provider	SpringerLink
Author	Tsimboukakis, Nikos Tambouratzis, George
Copyright Year	2009
Abstract	The present paper investigates the application of the multi-layer perceptron (MLP) to the task of categorizing texts based on their authors’ style. This task is of particular importance for information retrieval applications involving very large document databases. The emphasis of this article is to determine the extent to which the MLP model can be fine-tuned to successfully analyse such data, uncovering the stylistic differences among authors. The MLP-based method is compared and contrasted to statistical techniques, such as discriminant analysis, that are widely used in stylistic studies. The comparison of the methods is based on their classification performance, to provide an objective evaluation of the advantages of each method. A second aim of the study presented here is to compare the effectiveness of distinct features in the task of uncovering the author identity for each method. To evaluate to a greater depth the effectiveness of the entire approach, the results of the proposed MLP-based method are compared to those of established approaches, such as the support vector machines (SVM), using both the original parameters employed by the MLP as well as term frequency–inverse document frequency (TF–IDF) parameters, and the cascade correlation approach. It is found that the proposed MLP-based approach possesses a number of advantages, such as high classification accuracy, broadly comparable to that of the SVM, coupled with the ability to algorithmically reduce the set of parameters used without adversely affecting the classification accuracy.
Starting Page	573
Ending Page	582
Page Count	10
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	19
Issue Number	4
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-11-01
Publisher Place	London
Access Restriction	Subscribed
Subject Keyword	Document classification Neural networks Feature selection Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Software Artificial Intelligence

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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