VEBAV - A Simple, Scalable and Fast Authorship Verification Scheme

We present VEBAV - a simple, scalable and fast authorship verification scheme for the Author Identification (AI) task within the PAN-2014 competition. VEBAV (VEctor-Based Authorship Verifier), which is a modification of our existing PAN-2013 approach, is an intrinsic one-class-verification method, based on a simple distance function. VEBAV provides a number of benefits as for instance the independence of linguistic resources and tools like ontologies, thesauruses, language models, dictionaries, spellcheckers, etc. Another benefit is the low runtime of the method, due to the fact that deep linguistic processing techniques like POS-tagging, chunking or parsing are not taken into account. A further benefit of VEBAV is the ability to handle more as only one language. More concretely, it can be applied on documents written in Indo-European languages such as Dutch, English, Greek or Spanish. Regarding its configuration VEBAV can be extended or modified easily by replacing its underlying components. These include, for instance the distance function (required for classification), the acceptance criterion, the underlying features including their parameters and many more. In our experiments we achieved regarding a 20%-split of the PAN 2014 AI-training-corpus an overall accuracy score of 65,83% (in detail: 80% for Dutch-Essays, 55% for Dutch-Reviews, 55% for English-Essays, 80% English-Novels, 70% for Greek- Articles and 55% for Spanish-Articles)