Maximal Repeats Enhance Substring-based Authorship Attribution

This article tackles the Authorship Attribution task according to the language independence issue. We propose an alternative of variable length character n-grams features in supervised methods: maximal repeats in strings. When character ngrams are by essence redundant, maximal repeats are a condensed way to represent any substring of a corpus. Our experiments show that the redundant aspect of n-grams contributes to the efficiency of character-based techniques. Therefore, we introduce a new way to weight features in vector based classifier by introducing n-th order maximal repeats (maximal repeats detected in a set of maximal repeats). The experimental results show higher performance with maximal repeats, with less data than n-grams based approach (approximately divided by a factor of 10).