A Classifier System for Author Recognition Using Synonym-Based Features

The writing style of an author is a phenomenon that computer scientists and stylometrists have modeled in the past with some success. However, due to the complexity and variability of writing styles, simple models often break down when faced with real world data. Thus, current trends in stylometry often employ hundreds of features in building classifier systems. In this paper, we present a novel set of synonym-based features for author recognition. We outline a basic model of how synonyms relate to an author’s identify and then build an additional two models refined to meet real world needs. Experiments show strong correlation between the presented metric and the writing style of four authors with the second of the three models outperforming the others. As modern stylometric classifier systems demand increasingly larger feature sets, this new set of synonym-based features will serve to fill this everincreasing need. “The least of things with a meaning is worth more in life than the greatest of things without it.” Carl Jung (1875 1961)