=Paper=
{{Paper
|id=Vol-1737/T4-9
|storemode=property
|title=Graph-based Approach to Text Alignment for Plagiarism Detection in Persian Documents
|pdfUrl=https://ceur-ws.org/Vol-1737/T4-9.pdf
|volume=Vol-1737
|authors=Mozhgan Momtaz,Kayvan Bijari,Mostafa Salehi,Hadi Veisi
|dblpUrl=https://dblp.org/rec/conf/fire/MomtazBSV16
}}
==Graph-based Approach to Text Alignment for Plagiarism Detection in Persian Documents==
https://ceur-ws.org/Vol-1737/T4-9.pdf
Graph-based Approach to Text Alignment for Plagiarism
Detection in Persian Documents
Mozhgan Momtaz Kayvan Bijari Mostafa Salehi Hadi Veisi
Faculty of New Sciences and Faculty of New Sciences and Faculty of New Sciences and Faculty of New Sciences and
Technologies Technologies Technologies Technologies
University of Tehran, University of Tehran, University of Tehran, University of Tehran,
Tehran Tehran Tehran Tehran
m.momtaz92@ut.ac.ir kayvan.bijari@ut.ac.ir mostafa_salehi@ut.ac.ir h.veisi@ut.ac.ir
ABSTRACT Categories of text alignment dataset are based on PAN
This paper presents a new approach for Persian plagiarism Competitions [12].
detection. This approach uses a graph structure as well as one of
the graph similarity methods (iterative methods) for similarity
detection of two Persian documents. In this approach, documents
are represented by a graph with specified length, then each part of Monolingual Source Retrieval
suspicious document is compared to that of the source document. Different
The graph is made if these parts have more common bigrams than kinds of
a predefined threshold. Once graphs are made, an iterative method documents
is used to find analogous nodes in graphs. Two graphs are marked plagiarism Cross-language Text alignment
as similar if they contain at least a certain number of similar
nodes. In order to evaluate the proposed method, it was run on
PAN2015 and PAN2016 Persian Text Alignment dataset. The
Plagdet score is defined to evaluate plagiarism detection methods
in PAN contest. The gained Plagdet of proposed method is 90%
on PAN2015 and 87% on PAN2016.
Translation Obfuscation
None Obfuscation
Summary Obfuscation
Random Obfuscation
None plagiarism
CCS Concepts
• Information systems➝ Plagiarism Detection software
• Computing methodologies➝ Graph-based
Keywords
Plagiarism Detection, Text Based Graph Representation, Text
Alignment.
Figure 1- Different kinds of documents plagiarism
1. INTRODUCTION
Nowadays, a large volume of information is a compound of The proposed method, converts every document to a number of
different types of contextual data such as books, articles and other parts with specific length using the graph approach idea and then
documents and this volume is growing increasingly. In many if necessary, it converts each part to a graph for precise plagiarism
cases, we need to identify either the duplicated documents or the examination, which creates graph based on simultaneous
ones which are near-copy documents among the many cases. In occurrence of the words in fixed window size. After this step, the
this regard, Plagiarism detection in documents is one of the main similarity between two graphs measured using node similarity
topics which gained attention among researchers in the recent measures, if the rate of similarity is more than the specified
years. The act of plagiarism is to use other author's writing or threshold, then that part is labeled as plagiarism. The proposed
ideas, without proper appreciation to the author or proper citation method was run on the PAN2015 dataset [6] and PAN2016
to the original source [3]. In the recent years, identifying dataset (Persian Plagdet2016 contest [5]).
plagiarism has become easier using different systems, but The rest of paper is organized as follows: Section 2 is devoted to
different types of plagiarism is still an issue. In some types of related works; Section 3 presents the graph-based methodology
plagiarism, the structure of the document is changed by for plagiarism detection. In Section 4, experimental evaluation of
rearranging the words or using synonym words. Therefore, the the proposed method is given, and finally Section 5 ends the paper
results of basic plagiarism detection methods are not acceptable. with conclusion and future works.
So the need for more sophisticated methods for plagiarism
detection is growing. Different kinds of plagiarism are shown in
figure 1.
2. RELATED WORK model and the solutions based on graph model are summarized in
In order to detect monolingual plagiarism, various methods have table 1.
been proposed. In this section, each of these methods is explained Table1- Some issues of the bag-of-words model and the
briefly. solutions based on graph-based model for plagiarism
Character-based methods: The most important method is the detection applications
fingerprint method. Fingerprint algorithms [20] consider the text Issues of bag-of-words Graph-based solution
as a series of characters and then they divide the characters in n- methods to model text for
series groups, the most important algorithms include 16-gram, 8- plagiarism detection
gram and 5-gram methods. In this method, the degree of similarity applications
depends on the number of similar characters in a string. Although
this method ends up with a good result in detecting plagiarism, but Ignoring order of the words Using directed graph (step 3
when plagiarism has some paraphrasing or modified words, this of proposed method)
method does not act in an effective way to detect plagiarism. Ignoring the structure of the Considering the whole
Structural-based methods: In the previous method, the only text sentence as a graph (step 2
attention is on words as features of the document. However, in the and 3 of proposed method)
structural-based methods [9], pays attention to the titles, Neglecting word synonyms Ability to add synonyms to
paragraphs, sections and resources. One of the most famous the graph corresponding text
structural-based methods is tree-based method, which gained (step 3 of proposed method)
much attention in the recent years. In the tree-structured model, a
two-layer model is defined that the top layer is for retrieving
documents and the bottom layer is considered for detecting
plagiarism between retrieved documents using the methods of 3. METHODOLOGY
similarity detection such as cosine similarity method. Any textual document can be presented via a corresponding
graph. Graph based representation of text is important because it
Classification-based methods: In this method the documents enables us to turn an unstructured text into a structural text, and
are classified based on specific words (or keywords) [19]. The then the advantageous of graph representation can be applied to
primary goal in this approach is to retrieve similar documents and text summarization, identifying similarities of the documents, and
to speed up the process of plagiarism detection. applications of text mining. For natural language processing
Semantic-Based methods: This method uses lexical network applications text graph of documents should be built. In a text
in order to find semantic similarity for plagiarism detection [17]. graph, nodes represent words of the document, and the edges
The most famous lexical network in English language is WordNet present the relation between different words. The relation of
[8]. By means of WordNet, it is possible to achieve more words can vary from application to application. The proposed
information about a special word. This method is effective when plagiarism detection method is consist of 5 steps that will be
plagiarism is done using synonym words. FarsNet [15] the Persian discussed further in the following.
equivalent of WordNet, is also proposed for Persian language. Step 1. preprocessing: normalization is one of the basic steps
Graph-based methods: In this method, the text will be in text mining and text processing. In the normalization process,
converted into a graph. Nodes in the graph can be words, phrases punctuations and stop words are removed. In this paper, we have
or even sentences of the text. Edges in the graph represent the link used Hazm package [10] for Persian text normalization.
or relation between nodes and they can show the semantic link Step 2. turn text to set of clauses: suspicious document and
between words or the simultaneous occurrence in one sentence the reference document are divided to a series of sentences. Each
[16]. This method will be discussed more in the proposed method sentence of the suspected document will be compared to all the
section. Converting a text into a graph, enables us to detect sentences of the reference document. In this step, a filtering will
plagiarism using the advantages of graph similarity algorithms be done on sentences in order to reduce runtime. Finally, if the
[7]. two sentences at least have the cosine similarity of 0.4, then they
In this paper, the proposed method is a combination of structural- go to the step of graph making process (this value is obtained
based method and graph-based method. Paying attention to the experimentally), otherwise the comparison will continue to other
structure of the text leads to detection of plagiarism in the sentences of the reference document.
document even if the plagiarism is the type in which the structure Step 3. creating corresponding graph: in graph creation
of the text is changed. step, each sentence will be converted to a graph. The nodes of the
One common and standard approach to model text document is graph are main and unique words, and in this graph, an edge will
bag-of-words. This model is suitable for capturing word be established between a specific word and 4 words before and
frequency [16]. Assuming that order of the word’s occurrence has after it in the document. Igraph package [11] is used for graph
nothing to do with its meaning; this model has a proper result in creation in this paper.
information retrieval. The drawback of this model is when it tries Step 4. plagiarism detection: when graph creation is
to find the reused text and plagiarism between different parts of complete, we are looking for nodes in the suspected document
the text, if a reused text is occurring by using synonym words then that is common with the node of the reference document. An
this model could not properly detect the reused text. Furthermore, iterative method based on simple idea indicates that the two
this model doesn’t express the meaning and the structure of the graphs are similar when they have similar nodes, and nodes are
text [16]. However, Graph representation is mathematical similar if they have analogous neighbors [18]. We use this method
constructs and can model different word’s relation and textual for our specific graph. Then we find their similarity using
structure of the documents [16]. Some issues of the bag-of-words equation 1:
the two nodes. In this case, if plagiarism is done by add and
( )
( ( ) ( )) )1( removal of the words, by considering minimum similarity
threshold, these changes do not have much negative impact on
Where B is primary neighbors of the common node in the
plagiarism detection. The result of Persian Plagdet contest is also
graph of reference document. And A is primary neighbors
reported in [1].
of the common node in the graph of suspicious document.
If the similarities between two nodes is greater than the threshold
α (α = 0.4), then that node is selected as the similar node. Finally, Preprocessing
if a sentence has more similar nodes than the threshold β
(β=1/3(the number of key words in suspicious document)), that
statement is labeled as one of the sentences which plagiarism has Document suspected Reference documents
occurred. α and β are thresholds that are achieved experimentally of plagiarism in the database
and they are based on performances of the system.
Step 5. Integrate plagiarism labeled Sentences: In this Separating text to a set of sentences
step, we integrate sentences with plagiarism label (output of step 4
of the algorithm) based on start and end offset of sentences in text.
This step important for granularity measure1 [2]. If there exist no
labeled sentences, we can assume that no plagiarism is occurred. Sentence 1 of suspected Sentence 1 of reference
document document
4. EXPERIMENTAL EVALUATION
In this section the results of the implementation of the proposed
method on the plagiarism data sets are given. Moreover, in the
following we are going to focus on analyzing results. The two Choosing words to build a graph based
data sets that were used for analyzing the proposed method are as on minimum similarity
follows.
1. Persian Text alignment dataset PAN2015: This dataset is
published on the website of PAN contest.
Comparing paired generated graphs with each other and
2. Persian Text Alignment dataset PAN2016: This dataset identify similar graphs. (Sentences of the similar graphs
contains 2749 training and testing documents and are related to will get similarity label). And Aggregation of similar
Persian Plagdet2016 international contest [14, 4, 13], which was labeled Sentences
organized by the Institute of Information and Communication
Technology (ICTRC) and contest results are available at the
contest site. Detecting the existence of plagiarism
4.1 Experimental Results or not
Table 2 shows the results of the implementation of the proposed Figure 2- diagram of detecting plagiarism
method on the datasets.
Table2- Experimental Results on Persian document dataset 5. CONCLUSION AND FUTURE WORK
Using a graph structure, we proposed a method to convert
dataset Precision Recall Palgdet unstructured text into graphs, Graph-based approach provides the
ability that takes advantage of the benefits of graph algorithms
PAN2015 0.91 0.89 0.90
and use them in natural language processing algorithms. In this
PAN2016(training) 0.90 0.89 0.89 paper, we discussed and analyzed the results of the generalized
method to detect plagiarism on the inner levels (Text Alignment).
PAN2016(contest) 0.89 0.85 0.87 By achieving the Benchmark of Plagdet 87% without using
linguistic corpora and grammatical rules, it is expected that more
works in graph based approaches achieve better results in
As shown in the results, according to the evaluation criteria, the plagiarism detection. Furthermore, being independent from rules
graph-based method has achieved favorable results without using and corpora enables this method to detect plagiarism in other
linguistic corpora and only due to the structure of the text. Graph languages. As a future work we want to increase the accuracy of
approach has unique features to detect similar documents. Among the algorithm to detect semantic plagiarism using FarsNet lexical
these features, one can mention paying attention to non-adjacent network. Since it is possible to add word's synonyms to the
words in a sentence. This feature makes plagiarism detection easy, corresponding graph of the document, by adding synonym words,
because plagiarism is done by rearranging words, but in the the accuracy of detecting semantic plagiarism is increased.
character-based methods attention is just on the relationship Another important category of modern plagiarism, is plagiarism
between adjacent words. Furthermore, another feature of the on summary of a text. Due to the flexibility of graph approach in
graph is considering the minimum threshold of similarity between detecting plagiarism, graph approach is also efficient in detecting
plagiarism on summary of a text.
1 The logarithm of the granularity to smooth its influence on the
overall score.
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