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\begin{document}
-\title{Improving plagiarism detection}
+\title{Diverse Queries and Feature Type Selection for Plagiarism Discovery}
%%% Please do not remove the subtitle.
\subtitle{Notebook for PAN at CLEF 2013}
This paper describes approaches used for the Plagiarism Detection task in PAN 2013 international competition
on uncovering plagiarism, authorship, and social software misuse.
We present modified three-way search methodology for Source Retrieval subtask and analyse snippet similarity performance.
-Next, we show changes in selected feature for text alignement which led to plagdet score improvement.
-The results of source retrieval show, that presented approach is adaptable in real-world plagiarism situations.
-Improved results for text alignment achieved in the competition overall third place.
+The results show, that presented approach is adaptable in real-world plagiarism situations.
+For the Detailed Comparison task, we discuss feature type selection and
+global postprocessing. Resulting performance is significantly better
+with the described modifications, and further improvement is still possible.
\end{abstract}
\section{Introduction}
-In PAN 2013 competition on plagiarism detection we participated in both the Source Retrieval
-and the Text Alignment subtask. In both tasks we adapted methodology used in PAN 2012.
+In PAN 2013\footnote{%
+See \cite{pan2013} for an overview of PAN 2013 plagiarism detection campaign.}
+ competition on plagiarism detection we participated in both the Source Retrieval
+and the Text Alignment subtasks. In both tasks we adapted methodology used in PAN 2012\footnote{%
+See \cite{pan2012} for an overview of PAN 2012 plagiarism detection campaign.} \cite{suchomel_kas_12}.
Section~\ref{source_retr} describes querying approach for source retrieval, where we used three different
types of queries. We present a new type of query based on text paragraphs.
-The query execution were controled by its type and by preliminary similarities
+The query execution was controlled by its type and by preliminary similarities
discovered during the searches.
-In section~\ref{text_alignment} we present modified common text feature fot text alignment.
-We also compare performance of both the previous and the modified algorithms.
-
+Section~\ref{text_alignment} describes our approach for the text alignment
+(pairwise comparison) subtask. We briefly introduce our system,
+and then we discuss the feature types, which are usable for pairwise comparison,
+including the evaluation of their feasibility for this purpose. We then describe
+the global (corpus-wide) optimizations used, and finally we discuss
+the results achieved and further development.
\input{simon-source_retrieval}
\input{yenya-text_alignment}
\section{Conclusions}
-
-Unfortunately the ChatNoir search engine does not support phrasal search, therefore it
+We introduced querying strategy with snippet similarity measure. %which approved to be competitive.
+In source retrieval subtask the strategy performed with the second best ratio
+of recall to the number of used queries.
+We focused our queries on selected parts of text
+and on parts with no discovered external similarities.
+Unfortunately the ChatNoir search engine currently does not support phrasal search, therefore it
is possible that evaluated results may be quite distorted in this manner.
+In the text alignment subtask, we have achieved a significant improvement
+with respect to our system from PAN 2012. Further development in this
+area is still possible. For a real-world system, however, a completely
+different set of parameters and heuristics needs to be used, as a result
+of plagdet score together with the structure of the competition corpus
+being too different from the real world.
+
\bibliographystyle{splncs03}
\begin{raggedright}
\bibliography{pan13-notebook}
https://www.fi.muni.cz/~kas/git / pan13-paper.git / blobdiff