-%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}
-
-%\subsection{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.
+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}
+
+\subsection{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.