• Task: Given two documents, are they written by the same author?
  • Input: [data]
  • Submission: [submit]


Authorship attribution is an important problem in many areas including information retrieval and computational linguistics, but also in applied areas such as law and journalism where knowing the author of a document (such as a ransom note) may be able to save lives. The most common framework for testing candidate algorithms is a text classification problem: given known sample documents from a small, finite set of candidate authors, which if any wrote a questioned document of unknown authorship? It has been commented, however, that this may be an unreasonably easy task. A more demanding problem is author verification where given a set of documents by a single author and a questioned document, the problem is to determine if the questioned document was written by that particular author or not. This may more accurately reflect real life in the experiences of professional forensic linguists, who are often called upon to answer this kind of question. It is the second year PAN focuses on the so-called author verification problem.

A note to forensic linguists: In order to bridge the gap between linguistics and computer science, we strongly encourage submissions from researchers from both fields. We understand that research groups with expertise in linguistics use manual or semi-automated methods and, therefore, they are not able to submit their software. To enable their participation, we will provide them with the opportunity to analyze the test corpus after the deadline of software submission (mid-April). Their results will be ranked in a separate list with respect to the performance of the software submissions and they will be entitled to describe their approach in a paper. In this framework, any scholar or research group with expertise in linguistics wishing to participate should contact the Task Chair.


Given a small set (no more than 5, possibly as few as one) of "known" documents by a single person and a "questioned" document, the task is to determine whether the questioned document was written by the same person who wrote the known document set.

For your convenience, we summarize the main contributions of the 2014 edition of the author identification task with respect to previous editions:

  • The output of your software must be composed of real (probability) scores rather than binary Y/N answers
  • The maximum number of documents of known authorship within a problem is 5 (instead of 10)
  • The evaluation measures used for ranking are (ROC) AUC and c@1 instead of recall, precision and F1
  • More languages/genres are represented in the corpus
  • The training/evaluation corpora are larger
  • It is possible (optionally) to submit a trainable version of your approach to be used with any given training corpus
  • The task definition is the same
  • The format of corpus and ground truth is the same
  • The positive/negative problems are equally distributed


To develop your software, we provide you with a training corpus that comprises a set of author verification problems in several languages/genres. Each problem consists of some (up to five) known documents by a single person and exactly one questioned document. All documents within a single problem instance will be in the same language and best efforts are applied to assure that within-problem documents are matched for genre, register, theme, and date of writing. The document lengths vary from a few hundred to a few thousand words.

The documents of each problem are located in a separate folder, the name of which (problem ID) encodes the language/genre of the documents. The following list shows the available languages/genres, their codes, and examples of problem IDs:

Language Genre Code Problem IDs
Dutch essays DE DE001, DE002, DE003, etc.
Dutch reviews DR DR001, DR002, DR003, etc.
English essays EE EE001, EE002, EE003, etc.
English novels EN EN001, EN002, EN003, etc.
Greek articles GR GR001, GR002, GR003, etc.
Spanish articles SP SP001, SP002, SP003, etc.

The ground truth data of the training corpus found in the file truth.txt include one line per problem with problem ID and the correct binary answer (Y means the known and the questioned documents are by the same author and N means the opposite). For example:

EN001 N
EN002 Y
EN003 N


Your software must take as input the absolute path to a set of problems. For each problem there is a separate sub-folder within that path including the set of known documents and the single unknown document of that problem (similarly to the training corpus). The software has to output a single text file answers.txt with all the produced answers for the whole set of evaluation problems. Each line of this file corresponds to a problem instance, it starts with the ID of the problem followed by a score, a real number in [0,1] inclusive, corresponding to the probability of a positive answer. That is, 0 means it is absolutely sure the questioned document is not by the author of the known documents, 1.0 means it is absolutely sure the questioned document and the known documents are by the same author, and 0.5 means that a positive and a negative answer are equally likely. The probability scores should be round with three decimal digits. Use a single whitespace to separate problem ID and probability score.
For example, an answers.txt file may look like this:

EN001 0.031
EN002 0.874
EN003 0.500


The participants’ answers will be evaluated according to the area under the ROC curve (AUC) of their probability scores.

In addition, the performance of the binary classification results (automatically extracted from probability scores where every score greater than 0.5 corresponds to a positive answer, every score lower than 0.5 corresponds to a negative answer, while 0.5 corresponds to an unanswered problem, or an "I don’t know" answer) will be measured based on c@1 (Peñas & Rodrigo, 2011):

  • c@1 = (1/n)*(nc+(nu*nc/n))


  • n = #problems
  • nc = #correct_answers
  • nu = #unanswered_problems

Note: when positive/negative answers are provided for all available problems (probability scores different than 0.5), then c@1=accuracy. However, c@1 rewards approaches that maintain the same number of correct answers and decrease the number of incorrect answers by leaving some problems unanswered (when probability score equals 0.5).

The final ranking of the participants will be based on the product of AUC and c@1.


Authorship attribution performance
FinalScore Team
0.566 Meta Classifier
0.490 Mahmoud Khonji and Youssef Iraqi
Khalifa University, United Arab Emirates
0.484 Jordan Fréry°, Christine Largeron°, and Mihaela Juganaru-Mathieu*
°Université de Lyon and *École Nationale Supérieure des Mines, France
0.461 Esteban Castillo°, Ofelia Cervantes°, Darnes Vilariño*, David Pinto*, and Saul León*
°Universidad de las Américas Puebla and *Benemérita Universidad Autónoma de Puebla, Mexico
0.451 Erwan Moreau, Arun Jayapal, and Carl Vogel
Trinity College Dublin, Ireland
0.450 Cristhian Mayor, Josue Gutierrez, Angel Toledo, Rodrigo Martinez, Paola Ledesma, Gibran Fuentes, and Ivan Meza
Universidad Nacional Autonoma de Mexico, Mexico
0.426 Hamed Zamani, Hossein Nasr, Pariya Babaie, Samira Abnar, Mostafa Dehghani, and Azadeh Shakery
University of Tehran, Iran
0.400 Satyam, Anand, Arnav Kumar Dawn, and Sujan Kumar Saha
Birla Institute of Technology, India
0.375 Pashutan Modaresi and Philipp Gross
pressrelations GmbH, Germany
0.367 Magdalena Jankowska, Vlado Kešelj, and Evangelos Milios
Dalhousie University, Canada
0.335 Oren Halvani and Martin Steinebach
Fraunhofer Institute for Secure Information Technology SIT, Germany
0.325 Baseline
0.308 Anna Vartapetiance and Lee Gillam
University of Surrey, UK
0.306 Robert Layton
Federation University, Australia
0.304 Sarah Harvey
University of Waterloo, Canada

A more detailed analysis of the detection performances can be found in the overview paper accompanying this task.

Task Committee