which scales to the size of the Web. This methodology is based on the fact that we do not\r
possess enough resources to download and effectively process the whole corpus.\r
In the case of PAN 2013 competition the corpus\r
-of source documents is the ClueWeb~\footnote{\url{http://lemurproject.org/clueweb09.php/}} corpus. \r
+of source documents is the ClueWeb\footnote{\url{http://lemurproject.org/clueweb09.php/}} corpus. \r
As a document retrieval tool for the competition we utilized the ChatNoir~\cite{chatnoir} search engine which indexes the English\r
subset of the ClueWeb. \r
The reverse engineering decision process reside in creation of suitable queries on the basis of the suspicious document\r
This is the last decision phase -- what to report.\r
If there is any continuous passage of reused text detected, the result document is reported\r
and the continuous passages in the suspicious document are marked as 'discovered' and no further processing\r
-of those parts is made. \r
+of those parts is done. \r
\r
\subsection{Querying}\r
Querying means to effectively utilize the search engine in order to retrieve as many relevant\r
documents as possible with the minimum amount of queries. We consider the resulting document relevant \r
if it shares some of text characteristics with the suspicious document. \r
\r
-We used 3 different types of queries~\footnote{We used similar three-way based methodology in PAN 2012 \r
-Candidate Document Retrieval subtask. However this time we completely replaced the headers based queries\r
+We used 3 different types of queries\footnote{We used similar three-way based methodology in PAN 2012 \r
+Candidate Document Retrieval subtask. However, this time we completely replaced the headers based queries\r
with paragraph based queries, since the headers based queries did not pay off in the overall process.}:\r
i) keywords based queries, ii) intrinsic plagiarism\r
based queries, and iii) paragraph based queries. Three main properties distinguish each type of query: i) Positional; ii) Phrasal; iii) Deterministic.\r
Deterministic queries for specific suspicious document are always the same no matter how many times we run the software. \r
On the contrary the software can create in two runs potentially different nondeterministic queries.\r
\r
-\subsubsection{Keywords Based Queries}\r
+\subsubsection{Keywords Based Queries.}\r
+The keywords based queries compose of automatically extracted keywords from the whole suspicious document.\r
+Their purpose is to retrieve documents concerning the same theme. Two documents discussing the \r
+same theme usually share a set of overlapping keywords. Also the combination of keywords in\r
+query matters. \r
+As a method for automated keywords extraction, we used a frequency based approach described in~\cite{suchomel_kas_12}.\r
+The method combines term frequency analysis with TF-IDF score~\cite{Introduction_to_information_retrieval}. As a reference\r
+corpus we used English web corpus~\cite{ententen} crawled by SpiderLink~\cite{SpiderLink} in 2012 which contains 4.65 billion tokens. \r
\r
-\subsubsection{Intrinsic Plagiarism Based Queries}\r
-\subsubsection{Paragraph Based Queries}\r
-\subsection{Search Control}\r
+Each keywords based query were constructed from five top ranked keywords consecutively. Each keyword were\r
+used only in one query. Too long keywords based queries would be over-specific and it would have resulted\r
+in a low recall. On the other hand having constructed too short (one or two tokens) queries would have resulted\r
+in a low precision and also possibly low recall since they would be too general.\r
\r
+In order to direct the search more at the highest ranked keywords we also extracted their \r
+most frequent two and three term long collocations. These were combined also into queries of 5 words.\r
+Resulting the 4 top ranked keywords alone can appear in two different queries, one from the keywords\r
+alone and one from the collocations. Collocation describes its keyword better than the keyword alone. \r
\r
-\subsection{Result Selection}\r
-\subsection{Snippet Control}\r
+The keywords based queries are non-positional, since they represent the whole document. They are also non-phrasal since\r
+they are constructed of tokens gathered from different parts of the text. And they are deterministic, for certain input\r
+document the extractor always returns the same keywords.\r
\r
+\subsubsection{Intrinsic Plagiarism Based Queries.}\r
+The second type of queries purpose to retrieve pages which contain similar text detected\r
+as different, in a manner of writing style, from other parts of the suspicious document.\r
+Such a change may point out plagiarized passage which is intrinsically bound up with the text. \r
+We implemented vocabulary richness method which computes average word frequency class value for \r
+a given text part. The method is described in~\cite{awfc}. The problem is that generally methods\r
+based on the vocabulary statistics work better for longer texts. According to authors this method\r
+scales well for shorter texts than other text style detection methods. \r
+Still the usage is in our case limited by relatively short texts. It is also difficult to determine\r
+what parts of text to compare. Therefore we used sliding window concept for text chunking with the \r
+same settings as described in~\cite{suchomel_kas_12}.\r
\r
+A representative sentence longer than 6 words was randomly selected among those that apply from the suspicious part of the document.\r
+An intrinsic plagiarism based query is created from the representative sentence leaving out stop words.\r
+\r
+The intrinsic plagiarism based queries are positional. They carry the position of the representative sentence in the document.\r
+They are phrasal, since they represent a search for a specific sentence. And they are\r
+nondeterministic, because the representative sentence is selected randomly. \r
\r
\r
\r
+\subsubsection{Paragraph Based Queries.}\r
+they were executed as the last asi az v search control\r
+it would be extremely difficult to detect a single sentence other way than by exhaustive searching methods\r
+\r
+\subsection{Search Control}\r
+neoptimalizujeme na spravne utvorene dotazy z klicovych slov - stoji to vice dotazu\r
+\r
+\r
+\subsection{Result Selection}\r
+\subsection{Snippet Control}\r
+\r
\r
https://www.fi.muni.cz/~kas/git / pan13-paper.git / blobdiff