Overview of the author identification task at PAN 2014

The author identification task at PAN-2014 focuses on author verification. Similar to PAN-2013 we are given a set of documents by the same author along with exactly one document of questioned authorship, and the task is to determine whether the known and the questioned documents are by the same author or not. In comparison to PAN-2013, a significantly larger corpus was built comprising hundreds of documents in four natural languages (Dutch, English, Greek, and Spanish) and four genres (essays, reviews, novels, opinion articles). In addition, more suitable performance measures are used focusing on the accuracy and the confidence of the predictions as well as the ability of the submitted methods to leave some problems unanswered in case there is great uncertainty. To this end, we adopt the c@1 measure, originally proposed for the question answering task. We received 13 software submissions that were evaluated in the TIRA framework. Analytical evaluation results are presented where one language-independent approach serves as a challenging baseline. Moreover, we continue the successful practice of the PAN labs to examine meta-models based on the combination of all submitted systems. Last but not least, we provide statistical significance tests to demonstrate the important differences between the submitted approaches

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)