-The approach Masaryk University team has used in PAN 2012 Plagiarism
-detection---detailed comparison sub-task is based on the same approach
-that we have used in PAN 2010 \cite{Kasprzak2010}. This time, we have
-used a similar approach, enhanced by several means
-
-The algorithm evaluates the document pair in several stages:
-
-\begin{itemize}
-\item intrinsic plagiarism detection
-\item language detection of the source document
-\begin{itemize}
-\item cross-lingual plagiarism detection, if the source document is not in English
-\end{itemize}
-\item detecting intervals with common features
-\item post-processing phase, mainly serves for merging the nearby common intervals
-\end{itemize}
-
-\section{Intrinsic plagiarism detection}
-
-Our approach is based on character $n$-gram profiles of the interval of
-the fixed size (in terms of $n$-grams), and their differences to the
-profile of the whole document \cite{pan09stamatatos}. We have further
-enhanced the approach with using gaussian smoothing of the style-change
-function \cite{Kasprzak2010}.
-
-For PAN 2012, we have experimented with using 1-, 2-, and 3-grams instead
-of only 3-grams, and using the different measure of the difference between
-the n-gram profiles. We have used an approach similar to \cite{ngram},
-where we have compute the profile as an ordered set of 400 most-frequent
-$n$-grams in a given text (the whole document or a partial window). Apart
-from ordering the set we have ignored the actual number of occurrences
-of a given $n$-gram altogether, and used the value inveresly
-proportional to the $n$-gram order in the profile, in accordance with
-the Zipf's law \cite{zipf1935psycho}.
-
-This approach has provided more stable style-change function than
-than the one proposed in \cite{pan09stamatatos}. Because of pair-wise
-nature of the detailed comparison sub-task, we couldn't use the results
-of the intrinsic detection immediately, so we wanted to use them
-as hints to the external detection.
-
-\section{Cross-lingual detection}
+Our approach in PAN 2012 Plagiarism detection---Detailed comparison sub-task
+is loosely based on the approach we have used in PAN 2010 \cite{Kasprzak2010}.
+
+%The algorithm evaluates the document pair in several stages:
+%
+%\begin{itemize}
+%\item intrinsic plagiarism detection
+%\item language detection of the source document
+%\begin{itemize}
+%\item cross-lingual plagiarism detection, if the source document is not in English
+%\end{itemize}
+%\item detecting intervals with common features
+%\item post-processing phase, mainly serves for merging the nearby common intervals
+%\end{itemize}
+
+%\section{Intrinsic plagiarism detection}
+%
+%Our approach is based on character $n$-gram profiles of the interval of
+%the fixed size (in terms of $n$-grams), and their differences to the
+%profile of the whole document \cite{pan09stamatatos}. We have further
+%enhanced the approach with using gaussian smoothing of the style-change
+%function \cite{Kasprzak2010}.
+%
+%For PAN 2012, we have experimented with using 1-, 2-, and 3-grams instead
+%of only 3-grams, and using the different measure of the difference between
+%the n-gram profiles. We have used an approach similar to \cite{ngram},
+%where we have compute the profile as an ordered set of 400 most-frequent
+%$n$-grams in a given text (the whole document or a partial window). Apart
+%from ordering the set, we have ignored the actual number of occurrences
+%of a given $n$-gram altogether, and used the value inveresly
+%proportional to the $n$-gram order in the profile, in accordance with
+%the Zipf's law \cite{zipf1935psycho}.
+%
+%This approach has provided more stable style-change function than
+%than the one proposed in \cite{pan09stamatatos}. Because of pair-wise
+%nature of the detailed comparison sub-task, we couldn't use the results
+%of the intrinsic detection immediately, therefore we wanted to use them
+%as hints to the external detection.
+
+\section{Cross-lingual Plagiarism Detection}