Plagiarism Detection / Text Reuse Detection

This task is divided into source retrieval and text alignment. You can choose to solve one or both of them.

Source Retrieval

Given a suspicious document and a web search API, your task is to retrieve all plagiarized sources while minimizing retrieval costs.
Training Corpus

To develop your software, we provide you with a training corpus that consists of suspicious documents. Each suspicious document is about a specific topic and may consist of plagiarized passages obtained from web pages on that topic found in the ClueWeb09 corpus.

Learn more » Download corpus


If you are not in possession of the ClueWeb09 corpus, we also provide access to two search engines which index the ClueWeb, namely the Lemur Indri search engine and the ChatNoir search engine. To programmatically access these two search engines, we provide a unified search API.

Learn more »

Note: To better separate the source retrieval task from the text alignment task, the API provides a text alignment oracle feature. For each document you request to download from the ClueWeb, the text alignment oracle discloses if this document is a source for plagiarism for the suspicious document in question. In addition, the plagiarized text is returned. This, way participation in the source retrieval task does not require the development of a text alignment solution. However, you are free to use your own text alignment, if you want to.


For your convenience, we provide a baseline program written in Python.

Download program

The program loops through the suspicious documents in a given directory and outputs a search interaction log. The log is valid with respect to the output format described below. You may use the source code for getting started with your own approach.


For each suspicious document suspicious-documentXYZ.txt found in the evaluation corpora, your plagiarism detector shall output an interaction log suspicious-documentXYZ.log which logs meta information about your retrieval process:

Timestamp   [Query|Download_URL]
1358326592  barack obama family tree
1358326605  barack obama genealogy

For example, the above file would specify that at 1358326592 (Unix timestamp) the query barack obama family tree was sent and that in the following three of the retrieved documents were selected for download before the next query was sent.

Performance Measures

Performance will be measured based on the following five scores as averages over each suspicious document:

  1. Number of queries submitted.
  2. Number of web pages downloaded.
  3. Precision and recall of web pages downloaded regarding actual sources of a suspicious document.
  4. Number of queries until the first actual source is found.
  5. Number of downloads until the first actual source is downloaded.

Measures 1-3 capture the overall behavior of a system and measures 4-5 assess the time to first result. The quality of identifying reused passages between documents is not taken into account here, but note that retrieving duplicates of a source document is considered a true positive, whereas retrieving more than one duplicate of a source document does not improve performance.

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Test Corpus

Once you finished tuning your approach to achieve satisfying performance on the training corpus, you should run your software on the test corpus.

During the competition, the test corpus will not be released publicly. Instead, we ask you to submit your software for evaluation at our site as described below.

After the competition, the test corpus is available including ground truth data. This way, you have all the necessities to evaluate your approach on your own, yet being comparable to those who took part in the competition.


We ask you to prepare your software so that it can be executed via a command line call.

> mySoftware -i path/to/corpus -o path/to/output/directory -t accessToken

You can choose freely among the available programming languages and among the operating systems Microsoft Windows and Ubuntu. We will ask you to deploy your software onto a virtual machine that will be made accessible to you after registration. You will be able to reach the virtual machine via ssh and via remote desktop. More information about how to access the virtual machines can be found in the user guide below:

PAN Virtual Machine User Guide »

Once deployed in your virtual machine, we ask you to access TIRA at, where you can self-evaluate your software on the test data.

Note: By submitting your software you retain full copyrights. You agree to grant us usage rights only for the purpose of the PAN competition. We agree not to share your software with a third party or use it for other purposes than the PAN competition.

Related Work

This task has been run since PAN'12; here is a quick list of the respective proceedings and overviews:

Text Alignment Corpus Construction


This task may be solved in two alternative ways:

Collection: Find real-world instances of text reuse or plagiarism, and annotate them.

Generation: Given pairs of documents, generate passages of reused or plagiarized text between them. Apply a means of obfuscation of your choosing.


We ask you to prepare your corpus so that its format corresponds to the previous PAN plagiarism corpora. An example for of a correctly formatted corpus can be downloaded here:

Download example corpus

Enclosed in the evaluation corpora, a file named pairs is found, which lists all pairs of suspicious documents and source documents to be compared. For each pair suspicious-documentXYZ.txt and source-documentABC.txt, your plagiarism detector shall output an XML file suspicious-documentXYZ-source-documentABC.xml which contains meta information about the plagiarism cases detected within:

<document reference="suspicious-documentXYZ.txt">
<feature ... />

For example, the above file would specify an aligned passage of text between suspicious-documentXYZ.txt and source-documentABC.txt, and that it is of length 1000 characters, starting at character offset 5 in the suspicious document and at character offset 100 in the source document.

Performance Measures

Performance will be measured by assessing the validity of your corpus in two ways.

Detection: Your corpus will be fed into the text alignment prototypes that have been submitted in previous years to the text alignment task. The performances of each text alignment prototype in detecting the plagiarism in your corpus will be measured using macro-averaged precision and recall, granularity, and the plagdet score.

Peer-review: Your corpus will be made available to the other participants of this task and be subject to peer-review. Every participant will be given a chance to assess and analyze the corpora of all other participants in order to determine corpus quality.


To submit your corpus, put it in a ZIP archive, and make it available to us via a file sharing service of your choosing, e.g., Dropbox, or Mega.

Related Work

The text alignment task has been run since PAN'09; here is a quick list of the respective proceedings and overviews:

Task Chair

Martin Potthast

Martin Potthast

Bauhaus-Universität Weimar

Task Committee

Tim Gollub

Bauhaus-Universität Weimar

Matthias Hagen

Bauhaus-Universität Weimar

Benno Stein

Benno Stein

Bauhaus-Universität Weimar

Paolo Rosso

Paolo Rosso

Universitat Politècnica de València