Authorship Attribution Based on Specific Vocabulary
In this article we propose a technique for computing a standardized Z score capable of defining the specific vocabulary found in a text (or part thereof) compared to that of an entire corpus. Assuming that the term occurrence follows a binomial distribution, this method is then applied to weight terms (words and punctuation symbols in the current study), representing the lexical specificity of the underlying text. In a final stage, to define an author profile we suggest averaging these text representations and then applying them along with a distance measure to derive a simple and efficient authorship attribution scheme. To evaluate this algorithm and demonstrate its effectiveness, we develop two experiments, the first based on 5,408 newspaper articles (Glasgow Herald) written in English by 20 distinct authors and the second on 4,326 newspaper articles (La Stampa) written in Italian by 20 distinct authors. These experiments demonstrate that the suggested classification scheme tends to perform better than the Delta rule method based on the most frequent words, better than the chi-square distance based on word profiles and punctuation marks, better than the KLD scheme based on a predefined set of words, and better than the naive Bayes approach.
authorship attribution performance experimentation text classifiaction lexical statistics