yenya: odkomentovany odstavce, ktere jsem zakomentoval kvuli zkraceni extended abstractu

diff --git a/yenya-detailed.tex b/yenya-detailed.tex
index 6e4ef03ed966adaa6df521c0cfdeb2f3f6ef0043..525a1d3e5fc9bf0e3a92b43da321a4bb58c933e1 100644 (file)
--- a/yenya-detailed.tex
+++ b/yenya-detailed.tex
@@ -6,68 +6,68 @@
 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}
-
-%\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.
 
 \subsection{Cross-lingual Plagiarism Detection}
 
-%For language detection, we used the $n$-gram based categorization \cite{ngram}.
-%We have computed the language profiles from the source documents of the
-%training corpus (using the annotations from the corpus itself). The result
-%of this approach was better than using the stopwords-based detection we have
-%used in PAN 2010. However, there were still mis-detected documents,
-%mainly the long lists of surnames and other tabular data. We have added
-%an ad-hoc fix, where for documents having their profile too distant from all of
-%English, German, and Spanish profiles, we have declared them to be in English.
-
-%For cross-lingual plagiarism detection, our aim was to use the public
-%interface to Google translate if possible, and use the resulting document
-%as the source for standard intra-lingual detector.
-%Should the translation service not be available, we wanted
-%to use the fall-back strategy of translating isolated words only,
-%with the additional exact matching of longer words (we have used words with
-%5 characters or longer).
-%We have supposed that these longer words can be names or specialized terms,
-%present in both languages.
-
-%We have used dictionaries from several sources, like
-%{\it dicts.info}\footnote{\url{http://www.dicts.info/}},
-%{\it omegawiki}\footnote{\url{http://www.omegawiki.org/}},
-%and {\it wiktionary}\footnote{\url{http://en.wiktionary.org/}}. The source
-%and translated document were aligned on a line-by-line basis.
+For language detection, we used the $n$-gram based categorization \cite{ngram}.
+We have computed the language profiles from the source documents of the
+training corpus (using the annotations from the corpus itself). The result
+of this approach was better than using the stopwords-based detection we have
+used in PAN 2010. However, there were still mis-detected documents,
+mainly the long lists of surnames and other tabular data. We have added
+an ad-hoc fix, where for documents having their profile too distant from all of
+English, German, and Spanish profiles, we have declared them to be in English.
+
+For cross-lingual plagiarism detection, our aim was to use the public
+interface to Google translate if possible, and use the resulting document
+as the source for standard intra-lingual detector.
+Should the translation service not be available, we wanted
+to use the fall-back strategy of translating isolated words only,
+with the additional exact matching of longer words (we have used words with
+5 characters or longer).
+We have supposed that these longer words can be names or specialized terms,
+present in both languages.
+
+We have used dictionaries from several sources, like
+{\it dicts.info}\footnote{\url{http://www.dicts.info/}},
+{\it omegawiki}\footnote{\url{http://www.omegawiki.org/}},
+and {\it wiktionary}\footnote{\url{http://en.wiktionary.org/}}. The source
+and translated document were aligned on a line-by-line basis.
 
 In the final form of the detailed comparison sub-task, the results of machine
 translation of the source documents were provided to the detector programs
@@ -126,14 +126,14 @@ consisting of at least 5 common features, with the maximum allowed gap
 inside the interval (characters not belonging to any common feature
 of a given valid interval) set to 3,500 characters.
 
-%We have also experimented with modifying the allowed gap size using the
-%intrinsic plagiarism detection: to allow only shorter gap if the common
-%features around the gap belong to different passages, detected as plagiarized
-%in the suspicious document by the intrinsic detector, and allow larger gap,
-%if both the surrounding common features belong to the same passage,
-%detected by the intrinsic detector. This approach, however, did not show
-%any improvement against allowed gap of a static size, so it was omitted
-%from the final submission.
+We have also experimented with modifying the allowed gap size using the
+intrinsic plagiarism detection: to allow only shorter gap if the common
+features around the gap belong to different passages, detected as plagiarized
+in the suspicious document by the intrinsic detector, and allow larger gap,
+if both the surrounding common features belong to the same passage,
+detected by the intrinsic detector. This approach, however, did not show
+any improvement against allowed gap of a static size, so it was omitted
+from the final submission.
 
 \subsection{Postprocessing}
 
@@ -177,6 +177,6 @@ In the full paper, we will also discuss the following topics:
 \end{itemize}
 
 \nocite{pan09stamatatos}
-%\nocite{ngram}
+\nocite{ngram}