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\title{Three way search engine queries with multi-feature document comparison for plagiarism detection}
%%% Please do not remove the subtitle.
\subtitle{Notebook for PAN at CLEF 2012}

\author{\v{S}imon Suchomel \and Jan Kasprzak \and Michal Brandejs}
\institute{Faculty of Informatics, Masaryk University \\
{\tt\{suchomel,kas,brandejs\}@fi.muni.cz}}

\maketitle

\begin{abstract}
Briefly describe the main ideas of your approach.
\end{abstract}


\section{Introduction}

%The notebooks shall contain a full write-up of your approach, including all details necessary to reproduce your results.


Due to the increasing ease of plagiarism the plagiarism detection has nowadays become a need for many institutions.
Especially for universities where modern learning methods include e-learning and a vast document sources are online available.
%In the Information System of Masaryk University~\cite{ismu} there is also an antiplagiarism tool which is based upon the same principles as are shown in this paper.
The core methods for automatic plagiarism detection, which also work in practice on extensive collections of documents,
are based on computation document similarities. In order to compute a similarity
we need to possess the original and the plagiarized document.
%The most straightforward method is to use an online search engine in order to enrich
%document base with potential plagiarized documents and evaluate the amount of plagiarism by detailed document comparison. 
%In this paper we introduce a method which has been used in PAN 2012 competition\footnote{\url{http://pan.webis.de/}}
%in plagiarism detection.
In the first section we will introduce methods for candidate document retrieval from online sources, which took part in
PAN 2012  competition\footnote{\url{http://pan.webis.de/}} in plagiarism detection. 
The task was to retrieve a set of candidate source documents that may had served as an original to plagiarize from.
In the PAN 2012 candidate document retrieval test corpus, there were 32 text documents all contained at least one plagiarism case.
The documents were approximately 30 KB of size, the smallest were 18 KB and the largest were 44 KB.

In the second section we describe our approach of detailed document comparison.
 
We also discuss the performance ...



\include{simon-searchengine}
\include{yenya-detailed}

\section{Conclusions}

We present methods for candidate document retrieval which lead to
discovery the decent amount of plagiarism with minimizing the number of used queries. 
The proposed methods are applicable in general to any type of text input with no apriori information about the input document.
In PAN 2012 competition the proposed methods succeeded with similar amount of plagiarism detected with
only a small fraction of used queries compared to the others.  
 

   


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