\section{Source Retrieval}\r
+The source retrieval is a subtask in a plagiarism detection process during\r
+which only a relatively small subset of documents are retrieved from the\r
+large corpus. Those candidate documents are usually further compared in detail with the\r
+suspicious document. In the PAN 2013 source retrieval subtask the main goal was to\r
+identified web pages which have been used as a source of plagiarism for creation of the \r
+test corpus. \r
+The test corpus contained XX documents each discussing one and only one theme.\r
+Those documents were created intentionally by\r
+ semiprofessional writers, thus they feature nearly realistic plagiarism cases. \r
+ Such conditions are similar to a realistic plagiarism detection scenario, such as for\r
+state of the art commercial plagiarism detection systems or the anti-plagiarism service developed on and\r
+utilized at the Masaryk University. The main difference between real-world corpus \r
+of suspicious documents such as for example corpus created from theses stored in Information System of Masaryk University\r
+and the corpus of suspicious documents used during the PAN 2013 competition is that in the PAN\r
+corpus each document contains plagiarism passages. Therefore we can deepen the search during the process\r
+in certain parts of the document where no similar passage has yet been found. This is the main\r
+idea of improving recall of detected plagiarism in a suspicious document.\r
+\r
+\r
+\begin{figure}\r
+ \centering\r
+ \includegraphics[width=1.00\textwidth]{img/source_retrieval_process.pdf}\r
+ \caption{Source retrieval process.}\r
+ \label{fig:source_retr_process}\r
+\end{figure}\r
+\r
+An online plagiarism detection can be viewed as a reverse engineering task where \r
+we need to find original documents from which the plagiarized document was created.\r
+During the process the plagiarist locates original documents with the use of a search engine.\r
+The user decides what query the search engine to ask and which of the results from the result page to use.\r
+In real-world scenario the corpus is the whole Web and the search engine can be a contemporary commercial search engine\r
+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
+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
+and in decision what to actually download and what to report as a plagiarism case from the search results.\r
+\r
+These first two stages can be viewed in figure~\ref{fig:source_retr_process} as Querying and Selecting. Selected results \r
+from the search engine are forthwith textually aligned with the suspicious document (see section~\ref{text_alignment} for more details).\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 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
+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
+Positional queries carry extra information about a textual interval in the suspicious document which the query represents.\r
+A phrasal query aims for retrieval of documents containing the same small piece of a text. They are usually created from closely coupled words. \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
+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
+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
+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