The identification of authorship falls into the category of style classification, an interesting sub-field of text categorization that deals with properties of the form of linguistic expression as opposed to the content of a text. Various fea-ture sets and classification methods have been proposed in the literature, geared towards ab-stracting away from the content of a text, and focusing on its stylistic properties. We demon-strate that in a realistically difficult authorship attribution scenario, deep linguistic analysis features such as context free production fre-quencies and semantic relationship frequencies achieve significant error reduction over more commonly used “shallow” features such as function word frequencies and part of speech trigrams. Modern machine learning techniques like support vector machines allow us to ex-plore large feature vectors, combining these dif-ferent feature sets to achieve high classification accuracy in style-based tasks.
|Publisher||International Conference on Computational Linguistics|
Copyright COLING 2004