Authorship Attribution based on Data Compression for Telugu Text | Zendy

S. Nagaprasad | Zendy; P. Vijayapal Reddy | Zendy; A. Vinaya Babu | Zendy

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Authorship Attribution based on Data Compression for Telugu Text

Author(s) -

S. Nagaprasad,

P. Vijayapal Reddy,

A. Vinaya Babu

Publication year - 2015

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/19277-0686

Subject(s) - telugu , computer science , natural language processing , authorship attribution , compression (physics) , information retrieval , attribution , artificial intelligence , psychology , social psychology , materials science , composite material

Authorship attribution (AA) can be defined as the task of inferring characteristics of a document’s author from the textual characteristics of the document itself. In this paper we evaluated the compression model for AA on Telugu text. We considered six different compressors namely Zip, BZip, GZip, LZW, PPM and PPMd in combination with three different compression distance measures such as Normalized Compressor Distance (NCD), Compression Dissimilarity Measure (CDM) and Conditional Complexity of Compression (CCC). The result shows that the compression models are good alternatives for Authorship attribution instead of classification model with various features.

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