\section{Candidate document retrieval}~\label{simon}
-The basic concept of candidate document retrieval is to use a web search
+The basic concept of candidate document retrieval problem is to use a Web search
engine to find suitable documents. In PAN 2012 competition we used ChatNoir search
-engine~\cite{chatnoir} which indexes the entire English part of the ClueWeb09 corpus.
-We can now reduce the problem to constructing appropriate queries for ChatNoir search engine.
-The goal is to retrieve similar documents, which contains the same passages as the source document
+engine~\cite{chatnoir} which indexes the entire English part of the ClueWeb09~\footnote{\url{http://boston.lti.cs.cmu.edu/clueweb09/wiki/}} corpus.
+We can now reduce the problem to construct appropriate queries for ChatNoir search engine.
+The goal is to retrieve similar documents, which contain the same passages as the source document
or similar documents dealing with the same theme.
-The strongest emphasis has been places on minimizing the number of executed queries
-since they may be charged or time limited or number limited in the real world.
-
+The strongest emphasis has been placed on minimizing the number of executed queries
+since they may be charged, time limited or number limited in the real-world.
+For instance if we find a similarity for certain part of the document, we do not search
+for more similarities in that part.
%All queries from a given document were firstly preconstructed and they were afterwards sequentially executed
%according to their priority. After each executed query the intermediate results of plagiarism
%were calculated using the multi feature document comparison techniques described in section~\ref{yenya}.
%This process leads to continuing lowering the number of executed queries, for more details see the following passages.
-
-
We can divide constructed queries into three main groups according to their origin:
i) document keywords based, ii) intrinsic plagiarism based, and iii) headers based.
%Please note two major attributes of the extracted keywords. Firstly we say
\subsection{Keywords based queries}
-The document keywords based queries are constructed from automatically extracted keywords from the given document.
+The keywords based queries are constructed from automatically extracted keywords from the given document.
As for keyword extraction we used methodology based on word repetition similar to method described by Scott~\cite{text_patterns}.
The basic principle claims that the more frequent a single word in a given document is, the more likely it is to be key in it.
However the most frequent words usually do not carry any meaning.
For example articles (the), conjunctions (as) or prepositions (at).
These types of words are usually refered as the stop words.
-Thus we firstly filter out stop words according to general English stop words list.
-Secondly we used reference corpus word list as an additional filter.
+Therefore we firstly filtered out the stop words according to a general English stop words list.
+Secondly we used a reference corpus word list as an additional filter.
We used words in their simple verbatim occurrence allied to a
statistical estimate of likelihood. As a result of this, a word to become a key
-must not exists in the stop list and must occur in the suspicious document relatively much more frequently
-than in the reference corpus. We used a corpus created from common English web sources, which contains
+must not exist in the stop list and must occur in the suspicious document relatively much more frequently
+than in the reference corpus. We used the corpus created from common English Web sources, which contains
more then 4 billion tokens~\cite{ententen}.
The statistical measure TF-IDF~\cite{Introduction_to_information_retrieval}
was applied in order to decide whether the processed word shall become a keyword.
The TF-IDF combines term frequency (TF) and inverse document frequency (IDF) to produce composite weight of a given term.
-Taking into account the average length of the suspicious document and minimizing the number of used queries we set TF-IDF weight threshold to 0.02,
+Taking into account the average length of the suspicious document and minimizing the number of used queries we set the TF-IDF weight threshold to 0.02,
which resulted in about 10 to 20 terms identified as keywords per document.
-Before executing web search tasks we must combine extracted keywords into befitting queries.
-All queries were constructed from 5 keywords according to their TF-IDF weight consecutively.
-In other words 5 top ranked keywords combine into the first query the other 5 top ranked keywords combine into the second query.
+Before executing Web search tasks we must combine extracted keywords into befitting queries.
+All keyword based queries were constructed from 5 keywords according to their TF-IDF weight consecutively.
+In other words 5 top ranked keywords combine into the first query, the other 5 top ranked keywords combine into the second query.
By using this method we usually obtained from 2 to 3 initial queries per document.
\subsubsection{Collocations}
In addition to this we enhanced subsequent queries by the most frequent collocations of top 4 keywords.
For those 4 keywords we extracted the most frequent two-word and three-word collocation occurring within the single suspicious document.
-A scope for collocates was a single sentence. We counted collocates as the most frequent neighbouring words within the scope also omitting stop words.
+A scope for finding collocates was a single sentence. We counted collocates as the most frequent neighbouring words
+within the scope also omitting stop words.
To take advantage of extracted collocations we combined them among selfs also into 5 word queries.
After omitting queries containing duplicated words we added, on average, two new queries to each suspicious document.
-Despite of being composed from collocations we count those queries also as the keywords based. Together with
+Despite of being composed from collocations we count them also as the keywords based queries. Together with
the queries created only from bare keywords, those queries were unconditionally executed as the initial queries. \\
Queries containing around 5 words should be optimal in order to retrieve highest mean average precision of results.
-It means we would like to extract from results of those queries as many documents as possible but still dealing
-with the similar theme as the source document. Expecting the order of words within a single query has small
+It means that we would like to extract as many results as possible which will still be dealing with the same theme as the source document.
+Expecting the order of words within a single query has small
impact on results, no other heuristics of combining keywords into queries were applied also
-in order to keep the query amount minimal. In 32 from 33 input test documents were more than a hundred resulting documents found using
-those initial queries, which gave a decent document base covering the source document theme for document comparison.
+in order to keep the query amount minimal. In 32 from 33 input test documents there were more than a hundred resulting documents found using
+the initial queries, which gave a decent document base covering the source document theme for document comparison.
%using less kw in q would result into more general or underspecifi
All those previously mentioned queries characterize the document as a whole.
-They should cover the theme of the document expecting the single document is actually dealing with a one theme.
+They should cover the theme of the document expecting the single document is actually dealing with one theme.
\subsection{Intrinsic plagiarism based queries}
Intrinsic plagiarism queries were constructed from a representative sentence residing in
-the selected part of the document. Representative sentence is any sentence from the selected part of the document
+the selected part of the document. The representative sentence is any sentence
which is greater then eight words in length.
Such a sentence should produce the least searching false-positives~\cite{knight}.
The main task concerning representative sentences is to locate the suitable text part.
This part should be located by a method leading to discover potential plagiarism inside the document of its own,
-which is called intrinsic plagiarism detection. Such a method is based on for example text statistics or syntactic features.
+which is called intrinsic plagiarism detection. Such a method is based on e.g. text statistics or syntactic features.
We chose statistical vocabulary richness method called average word frequency class described in~\cite{awfc}.
-Using this method we can compute average word frequency class values (AWFC) for arbitrary parts of the text. The change
-of this value between two adjacent text windows indicates change of writing style, which can be caused by a plagiarism.
-We computed AWFC value for every text window of size 45 words, which was shifted through the text by 40 words span.
-The values of window size and span of shifting was set empirically during training. Windows with
-largest change of AWFC compared to AWFC of their neighbouring previous window were selected as candidate for query extraction.
+By using this method we can compute average word frequency class values (AWFC) for arbitrary parts of the text. The change
+of this value between two adjacent text windows indicates change of the writing style, which can be caused by a plagiarism.
+We computed AWFC value for every text window of 45 words size, which was shifted through the text by 40 words span.
+The both values were set empirically during training. Windows with
+largest change of AWFC compared to AWFC of their neighbouring previous window were selected as a candidate for query extraction.
-The query from the representative sentence was composed by 6 words from the sentence beginning, also omitting stop words.
-We chose 6 words to cover higher portion of selected sentence and not to exceed
+The query from the representative sentence was composed by 6 words from the beginning of the sentence, also omitting stop words.
+We chose 6 words to make more specific queries and not to exceed
the phrasal ChatNoir query limit.
-This type of query is actually looking for documents containing the very same or at least the very similar sentence,
-therefore we choose more than 5 word queries in order to narrow the search to more specific
-results. Unfortunately in the time of PAN 2012 competition the ChatNoir search engine did not
-support phrasal search. Since querying a single sentence is phrasal search, one might have
-expected even better results from those queries.
+%This type of query is actually looking for documents containing the very same or at least the very similar sentence,
+%therefore we chose more than 5 word queries in order to narrow the search to more specific results.
+Unfortunately in the time of PAN 2012 competition the ChatNoir search engine did not
+support phrasal search. Since querying a single sentence is a phrasal search, one might have
+expected to achieve even better results from those queries.
%Due to the fact, that ChatNoir does not currently support phrasal search,
The intrinsic plagiarism based queries are document positionable, meaning that we can store
-their position within the document. It is due to the fact, that
-those queries are created from more or less subsequent words. As a result, they are also executed conditionally, for more
-information see section~\ref{process}.
-The position within the document were set to the
-start position of the representative sentence.
+their position within the document. It is due to the fact that
+those queries are created from more or less subsequent words. As a result of that, they are executed conditionally, for more
+information see Section~\ref{process}.
They characterize only a specific portion of the suspicious document on the contrary to keyword based queries.
+The positions within the document were set to the
+start positions of the representative sentences.
+
\subsection{Headers based queries}
The last type of queries were constructed from local headers of the document.
-A header usually characterize briefly and accurately the following section, thus it
+A header usually characterizes briefly and accurately the following section, thus it
can be used as a search query. In real scenarios the headers should
-be located using some document metadata, in that way we can distinguish for example more header levels.
-In the case of PAN 2012 competition we have detected headers according to several
-basic rules: As a headers based query we considered any line which has an empty line above and bellow the actual line
-and is from 2 to 6 words in length also omitting stop words.
-Those basic rules applied on most headers from given format of test corpus text files.
+be located using some document metadata. In that way we can distinguish for example more header levels.
+In the case of PAN 2012 competition we constructed headers based queries according to several
+basic rules: i) the query is created from any line which has an empty line above and bellow,
+ii) it is from 2 to 6 words in length also omitting stop words.
+These basic rules applied on most headers from the given format of test corpus text files.
-Note that length of header based queries are variable, thus they can be both specific
-and general according to their length. They are also document positionable.
-We calculated their position as start position of the following text.
+Note that the length of headers based queries are variable, thus they can be both more specific
+and more general.% according to their length.
+They are also document positionable.
+We calculated their position as the start position of the following text.
\subsection{Combining and executing queries}~\label{process}
After each executed query the intermediate results of plagiarism
-were calculated using the multi feature document comparison techniques described in section~\ref{yenya}.
-The queries processing is outlined as Algorithm~\ref{alg:queries}, where for simplicity the snippet calculation and a web source download is omitted.
+were calculated using the multi feature document comparison techniques described in Section~\ref{yenya}.
+The queries processing is outlined as Algorithm~\ref{alg:queries}, where for simplicity the snippet calculation and a Web source download is omitted.
After executing a query the intermediate results were always
calculated as follows:
For every query result in result page based
on a snippet to input suspicious document similarities, we decided whether to actually
-download the resulting URL or not. Since the snippet contains portions of Web document to each
+download the resulting URL or not. Since the snippet contains portions of the Web document to each
given keyword, we calculated pre-similarity as apportionment of every word match between the snipped and the suspicious document.
-If there were at least 80\% match, we downloaded the web source for thorough investigation.
+If there were at least 80\% match, we downloaded the Web source for thorough investigation.
-For each downloaded web document we calculated similarities with the input suspicious document
-as a document pair described in section~\ref{yenya}.
+For each downloaded Web document we calculated similarities with the input suspicious document
+as a document pair described in Section~\ref{yenya}.
All similarities were stored in form of intervals from within
the input suspicious document. In other words for every similar part between the downloaded
document and the suspicious document we stored the beginning position and the ending position of that part in
\REQUIRE suspicious document $d$, queries $Q_{KW}, Q_{Intrinsic}, Q_{Headers}$
%keyword, intrinsic and headers based queries $Q_{KW}, Q_{Intrinsic}, Q_{Headers}$,
-\ENSURE plagiarism source candidate web documents $W_{plag}$
+\ENSURE plagiarism source candidate Web documents $W_{plag}$
\medskip
\STATE $I \leftarrow \emptyset; I \in \mathbb{N}^2$
\subsection{Queries comparison}~\label{comparison}
During the test phase there were extracted 133 keywords based queries, 165 intrinsic plagiarism
-based queries and 331 headers based queries in total. Table~\ref{querycount} compares
+based queries and 331 headers based queries from the test corpus in total. Table~\ref{querycount} compares
results according to query types.
\begin{center}
\begin{table}[h]
\begin{tabular}{|c|c|c|c|}
\hline
{\bf Query type} & {\bf Extracted}& {\bf Omitted} & {\bf Similarities portion }\\ \hline \hline
-KW & 4.16 & N/A & 72.5 \% \\ \hline
-Intrinsic & 5.16 & 2.35 & 24.3 \% \\ \hline
-Headers & 10.34 & 4.75 & 3.2 \% \\ \hline
+KW & 4.16 & N/A & 72.5\% \\ \hline
+Intrinsic & 5.16 & 2.35 & 24.3\% \\ \hline
+Headers & 10.34 & 4.75 & 3.2\% \\ \hline
\end{tabular}\\
\end{center}
\vspace{0.3 cm}
headers based queries as the least successful. Despite the fact, that they were greatest
in number they ended with only more than a 3\% of total similarities found. Nevertheless, please
note that the headers based queries were executed as the last, thus they were used only for
-finding undiscovered potential similarities. In order to really compere the query type performance, we
+finding undiscovered potential similarities. In order to really compare the query type performance, we
would need to execute and evaluate them separately.
To conclude this section we can say, that all types of queries were more or less successful. The headers based
were executed last and in the process they were the least successful. The interesting
finding is the fact, that we can even greatly lower the number of executed queries.
-By omitting all of headers based queries we could lover the total number of executed queries by 45 \% with only
-3.2 \% of recall lost.
+By omitting all of headers based queries we could lover the total number of executed queries by 45\% with only
+3.2\% of recall lost.
%\begin{center}
+
+
+
\begin{table}[h]
\begin{center}
{ \scriptsize
-\begin{tabular}{c c c c c c c c c c c c }
+\begin{tabular}{l c c c c c c c c c c c }
\hline
-& \multicolumn{2}{c}{\bf Total workload} & \multicolumn{2}{c}{\bf Time to 1st Result}&\multirow{2}{*}{\parbox{0.6cm}{\bf No \\ result}} &
+& \multicolumn{2}{c}{\strut \bf Total workload} & \multicolumn{2}{c}{\bf Time to 1st Result}&\multirow{2}{*}{\parbox{0.6cm}{\bf No \\ result}} &
\multicolumn{2}{c}{\bf Reported Srcs.} & \multicolumn{2}{c}{\bf Downloaded Srcs.} &
- \multicolumn{2}{c}{\bf Retrieved Srcs.} \\
-{\bf Team}&{\bf Queries}&{\bf Downloads}&{\bf Queries}&{\bf Downloads}
+ \multicolumn{2}{c}{\bf Retrieved Srcs.} \\
+
+{\bf Team \strut}&{\bf Queries}&{\bf Downloads}&{\bf Queries}&{\bf Downloads}
& & {\bf Prec.}&{\bf Recall}&{\bf Prec.}&{\bf Recall}&{\bf Prec.}&{\bf Recall}\\ \hline \hline
-\parbox{2,3cm}{Gillam et al. \\ University of Surrey, UK} & 63.44 & 527.41 & 4.47 &
- 25.88 & {\bf 1} & 0.6266 & 0.2493 & 0.0182 & {\bf 0.5567} & 0.0182 & {\bf 0.5567} \\ \hline
+\parbox{2,3cm}{\strut Gillam et al. \\ University of Surrey, UK \strut} & 63.44 & 527.41 & 4.47 &
+ 25.88 & {\bf 1} & 0.6266 & 0.2493 & 0.0182 & {\bf 0.5567} & 0.0182 & {\bf 0.5567} \\ %\hline
-\parbox{2,3cm}{ Jayapal \\ University of Sheffield, UK }& 67.06 & 173.47 & 8.78 & 13.50 &
- 1 & {\bf 0.6582} & {\bf 0.2775} & 0.0709 & 0.4342 & {\bf 0.0698} & 0.4342 \\ \hline
+\parbox{2,3cm}{\strut Jayapal \\ University of Sheffield, UK \strut}& 67.06 & 173.47 & 8.78 & 13.50 &
+ 1 & {\bf 0.6582} & {\bf 0.2775} & 0.0709 & 0.4342 & {\bf 0.0698} & 0.4342 \\ %\hline
- \parbox{2,3cm}{Kong Leilei \\ Heilongjiang Institute of Technology,\\ China} & 551.06 &
- 326.66 & 80.59 & 27.47 & 2 & 0.5720 & 0.2351 & 0.0178 & 0.3742 & 0.0141 & 0.3788 \\ \hline
+ \parbox{2,3cm}{\strut Kong Leilei \\ Heilongjiang Institute of Technology,\\ China \strut } & 551.06 &
+ 326.66 & 80.59 & 27.47 & 2 & 0.5720 & 0.2351 & 0.0178 & 0.3742 & 0.0141 & 0.3788 \\ %\hline
-\parbox{2,3cm}{Palkovskii et al. \\ Zhytomyr State University, Ukraine} & 63.13 &
-1026.72 & 27.28 & 318.94 & 6 & 0.4349 & 0.1203 & 0.0025 & 0.2133 & 0.0024 & 0.2133 \\ \hline
+\parbox{2,3cm}{\strut Palkovskii et al. \\ Zhytomyr State University, Ukraine \strut} & 63.13 &
+1026.72 & 27.28 & 318.94 & 6 & 0.4349 & 0.1203 & 0.0025 & 0.2133 & 0.0024 & 0.2133 \\ %\hline
-\parbox{2,3cm}{\bf our approach}& {\bf 12.56} & {\bf 95.41} & {\bf 1.53} & {\bf 6.28} & 2 & 0.5177 & 0.2087 & {\bf 0.0813} &
+\parbox{2,3cm}{\strut \bf our approach \strut }& {\bf 12.56} & {\bf 95.41} & {\bf 1.53} & {\bf 6.28} & 2 & 0.5177 & 0.2087 & {\bf 0.0813} &
0.3513 & 0.0094 & 0.4519 \\ \hline
\end{tabular}\\}
\end{center}
Table~\ref{candidateDocsResults} shows results of PAN 2012 candidate document retrieval
task as averages over the all 32 documents from the test corpus. Our approach led
-to obtain decent retrieval performance with very little total workload and time to 1st result.
-Also the 80 \% word match treshold in Web snippet appear to be suitable, since we
+to obtain decent retrieval performance with the minimal total workload and minimal time to 1st result.
+Also the 80\% word match threshold in Web snippet appears to be suitable, since we
also achieved the highest precision among downloaded sources.
https://www.fi.muni.cz/~kas/git / pan12-paper.git / blobdiff