density and length.
As a proof of concept, we used two types of common features: word
-5-grams and stop-word 8-grams, the later being based on the method described in
+5-grams and stop word 8-grams, the later being based on the method described in
\cite{stamatatos2011plagiarism}.
In addition to the above, we made several minor improvements to the
-algorithm, such as parameter tuning and improving the detections
+algorithm such as parameter tuning and improving the detections
merging in the post-processing stage.
\subsection{Algorithm Overview}
We tokenize the document into words, where word is a sequence of one
or more characters of the {\it Letter} Unicode class.
-With each word, two additional attributes, needed for further processing,
+With each word, two additional attributes needed for further processing,
are associated: the offset where the word begins, and the word length.
The offset where the word begins is not necessarily the first letter character
-of the word itself. We discovered that in the training corpus,
+of the word itself. We discovered that in the training corpus
some plagiarized passages were annotated including the preceding
non-letter characters. We used the following heuristics to add
-parts of the inter-word gap to the previous, or the next adjacent word:
+parts of the inter-word gap to the previous or the next adjacent word:
\begin{itemize}
\item When the inter-word gap contains interpunction (any of the dot,
-semicolon, colon, comma, exclamation mark, question mark, or quotes),
-add the characters up to, and including the interpunction, to the previous
-word, ignore the space character(s) after the interpunction, and add
-the rest to the next word.
-\item Otherwise, when the inter-word gap contains newline, add the character
-before the first newline to the previous word, ignore the first newline
-character, and add the rest to the next word.
-\item otherwise, ignore the inter-word gap characters altogether.
+semicolon, colon, comma, exclamation mark, question mark, or quotes):
+\begin{itemize}
+\item add the characters up to and including the interpunction character
+to the previous word,
+\item ignore the space character(s) after the interpunction
+character,
+\item add the rest to the next word.
+\end{itemize}
+\item Otherwise, when the inter-word gap contains newline:
+\begin{itemize}
+\item add the character before the first newline to the previous word,
+\item ignore the first newline character,
+\item add the rest to the next word.
+\end{itemize}
+\item Otherwise: ignore the inter-word gap characters altogether.
\end{itemize}
When the detection program was given three different
\begin{itemize}
\item Lexicographically sorted word 5-grams, formed of words at least
-three characters long, and
-\item unsorted stop-word 8-grams, formed from 50 most frequent words in English,
+three characters long.
+\item Unsorted stop word 8-grams, formed from 50 most frequent words in English,
as described in \cite{stamatatos2011plagiarism}. We have further ignored
the 8-grams, formed solely from the six most frequent English words
-(the, of, and, a, in, to), or the possessive {\it'{}s}.
+({\it the}, {\it of}, {\it and}, {\it a}, {\it in}, {\it to}), or the possessive {\it'{}s}.
\end{itemize}
We represented each feature with the 32 highest-order bits of its
-MD5 digest. This is only a performance optimization, targeted for
+MD5 digest. This is only a performance optimization targeted for
larger systems. The number of features in a document pair is several orders
-of magnitude lower than $2^{32}$, so the probability of hash function
+of magnitude lower than $2^{32}$, thus the probability of hash function
collision is low. For pair-wise comparison, it would be feasible to compare
the features directly instead of their MD5 sums.
-Each feature has also the offset and length attributes.
+Each feature has also two attributes: offset and length.
Offset is taken as the offset of the first word in a given feature,
and length is the offset of the last character in a given feature
minus the offset of the feature itself.
allows to use the ordering of features as a measure of distance.
When we use features of different types, there is no natural ordering
-of them: for example, a stop-word 8-gram can span multiple sentences,
+of them: for example a stop word 8-gram can span multiple sentences,
which can contain several word 5-grams. The assumption of both of the
above algorithms, that the last character of the previous feature
is before the last character of the current feature, is broken.
\subsection{Postprocessing}
\label{postprocessing}
-In the postprocessing phase, we took the resulting valid intervals,
+In the postprocessing phase we took the resulting valid intervals
and made attempt to further improve the results. We firstly
removed overlaps: if both overlapping intervals were
shorter than 300 characters, we have removed both of them. Otherwise, we
and it contained at least one feature per 10,000
characters\footnote{we have computed the length of the gap as the number
of characters between the detections in the source document, plus the
-number of charaters between the detections in the suspicious document.}, or
+number of charaters between the detections in the suspicious document.}
\item the gap was smaller than 30,000 characters and the size of the adjacent
-valid intervals was at least twice as big as the gap between them, or
+valid intervals was at least twice as big as the gap between them
\item the gap was smaller than 30,000 characters and the number of common
features per character in the adjacent interval was not more than three times
bigger than number of features per character in the possible joined interval.
intervals, if they both belong to the same passage, detected by
the intrinsic detector. This approach did not provide improvement
when compared to the static gap limits, as described in Section
-\ref{postprocessing}, so we have omitted it from our final submission.
+\ref{postprocessing}, therefore we have omitted it from our final submission.
%\subsubsection{Language Detection}
%
\subsubsection{Cross-lingual Plagiarism Detection}
For cross-lingual plagiarism detection, our aim was to use the public
-interface to Google translate if possible, and use the resulting document
+interface to Google Translate\footnote{\url{http://translate.google.com/}} 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,
https://www.fi.muni.cz/~kas/git / pan12-paper.git / commitdiff