A Generic Authorship Verification Scheme Based on Equal Error Rates

We present a generic authorship verification scheme for the PAN-2015 identification task. Our scheme uses a two-step training phase on the training corpora. The first phase learns individual feature category parameters as well as decision thresholds based on equal error rates. The second phase builds feature category ensembles which are used for majority vote decisions because ensembles can outperform single feature categories. All feature categories used in our method are very simple to gain multiple advantages: Our method is entirely independent of any external linguistic resources (even word lists), and hence it can easily applied to many languages. Moreover, the classification is very fast due to simple features. Additionally, we make use of parallelization. The evaluation of our scheme on a 40% split (which we did not use for training) of the official PAN2015 training corpus led to an average corpus accuracy of 68.12%; in detail 60% for the Dutch, 67.5% for the English, 60% for the Greek and 85% for the Spanish subcorpus. The overall computation runtime was approximately 27 seconds.