Octopus: University of The Aegean Research & Project Outputs System

Conference

Authors:	Houvardas J., Stamatatos E.
Title:	N-gram Feature Selection for Authorship Identification
Conference:	12th Int. Conf. on Artificial Intelligence: Methodology, Systems, Applications (AIMSA
Editors:	J. Euzenat, and J. Domingue
Ed:	No
Eds:	Yes
Pages:	77-86
To appear:	No
Month:
Year:	2006
Place:
Pubisher:
Link:
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Abstract:	Automatic authorship identification offers a valuable tool for supporting crime investigation and security. It can be seen as a multi-class, single-label text categorization task. Character n-grams are a very successful approach to represent text for stylistic purposes since they are able to capture nuances in lexical, syntactical, and structural level. So far, character n-grams of fixed length have been used for authorship identification. In this paper, we propose a variable-length n-gram approach inspired by previous work for selecting variable-length word sequences. Using a subset of the new Reuters corpus, consisting of texts on the same topic by 50 different authors, we show that the proposed approach is at least as effective as information gain for selecting the most significant n-grams although the feature sets produced by the two methods have few common members. Moreover, we explore the significance of digits for distinguishing between authors showing that an increase in performance can be achieved using simple text pre-processing.