Plagiarism detection is the process of locating text reuse within a suspicious document. The plagiarism detection corpora are used for evaluating plagiarism detection systems. In this paper, we present a bilingual PersianEnglish plagiarism detection corpus. We provide our corpus for the task of text alignment corpus construction in the PAN 2015 competition. Our approach is based on parallel corpus sentences. We have used a Persian-English sentence aligned parallel corpus in a combination with Wikipedia articles to create our corpus. Paired sentences in parallel corpus have a similarity score between 0 and 1. We have used similarity scores to establish the degree of obfuscation for constructing the plagiarism cases.
Plagiarism detection is the automatic identification of plagiarism and the retrieval of
the original sources [
The cross lingual plagiarism detection corpora are used to evaluate the cross
lingual plagiarism detection systems. Since the creation of plagiarism corpora is very
time demanding, so an alternative approach is to construct a corpus consisting of
artificial plagiarized passages [
In this paper, we have proposed an approach to construct a bilingual PersianEnglish plagiarism detection corpus by using a Persian-English parallel corpus. The parallel corpus consists of aligned parallel sentences with similarity scores. Sentence similarity scores have been used for establishing obfuscation degree in plagiarism cases. The paper is organized as follow: In section 2 we introduce the preparation of data sources needed to construct our corpus. In section 3 we will describe our approach in detail. Then, we will discuss the results of corpus building in section 4. Finally, we will conclude and explain about some future works in section 5. 2
We have used Wikipedia documents for constructing the main body of source and suspicious documents. Moreover, we exploited a parallel Persian- English sentence aligned corpus to construct the plagiarized passages. By inserting plagiarized passages with specific degrees of obfuscation into the document with related topics, a bilingual Persian–English plagiarism detection corpus was established. In the following subsections we provide a brief overview of these two resources. 2.1
Wikipedia is a rich multilingual web-based encyclopedia. Each document in
Wikipedia is represented as a page. The text of pages is partially structured [
We have exploited a parallel English-Persian sentence aligned corpus to construct paired plagiarism passages to be inserted into source (English) and suspicious (Persian) documents. A collection of 12 features were used into a Maximum Entropy (MaxEnt) log linear model in order to compute the similarity scores between paired sentences. The features are in four categories including: Features based on sentence length, Features related to dictionary (IBM model 1), Features based on alignment and, Miscellaneous features. The total score resulted from the mentioned features has been used to determine the various degrees of obfuscation in plagiarized passages; the more similar sentences can be used to build less obfuscated passages.
In this section we describe our approach to generate a bilingual Persian-English plagiarism detection corpus. We use a sentence aligned parallel corpus to create plagiarism cases. In the following, we explain our approach in five steps: preprocessing, clustering, building plagiarism cases, fragment obfuscation and inserting plagiarized cases into source and suspicious documents. 3.1
Persian is one of the Indo-European languages which have borrowed its script from
Arabic, a member of the Semitic language family [
Therefore, to overcome these problems and challenging issues, we have applied some algorithms such as normalization in the preprocessing stage of the system. Unification of letters to Unicode characters designed for Persian and using zero-width non-joiner space are applied in normalization algorithm. 3.2
Our purpose is to establish topically similarity between suspicious documents, source documents and their plagiarism cases, so as to make plagiarism corpus to be more realistic and make plagiarism cases hard to find.
We have proposed our approach for clustered parallel sentences and Wikipedia documents into different topically related groups. Therefore, this step is organized in two subsections: parallel sentence clustering and documents clustering. In the following, we describe the process of each subsection.
Parallel Sentence Clustering. Given a collection of parallel sentences, the clustering procedure of parallel sentences is accomplished to detect the presence of distinct groups and assign parallel sentences to groups, such that the parallel sentences within a group are very similar and also parallel sentences in apart clusters are different from one another.
Since the parallel corpus we have used, has been extracted from Wikipedia, so we used the structure of the wiki pages for clustering of sentences. The algorithm for clustering of parallel sentences is as follow: 1. Persian Wikipedia documents were indexed by the Apache Lucene library. 2. A query was built from each Persian sentence. 3. The query was searched in the indexed documents and returns the top document. 4. A bipartite graph of return documents-categories was created. Then, the info- map community detection algorithm was applied to the graph and all communities were detected. Documents within a community are considered as one cluster. 5. Finally, parallel sentences were assigned to the documents in the same cluster. Documents Clustering. For clustering of documents which includes source and suspicious documents, we used the results of parallel sentences clustering stage. For each cluster of return documents in the previous stage, the categories of documents have been extracted and considered as label of that cluster. Then, we collected basic documents into different topically related clusters based on their categories. The documents are assigned to the cluster with maximum common categories. 3.3
In this step, we have used paired sentences from parallel corpus to create plagiarism cases. For constructing a plagiarism case, we put together some of the sentences of parallel corpus. Note that source fragments were generated from sentences in the English language and plagiarized fragments were constructed by Persian sentences paired with English sentences.
The length of fragments is evenly distributed between 3 and 15 sentences. The length of fragments is shown in table 1. Plagiarism cases in bilingual corpus are constructed from parallel sentences. Plagiarized fragments have been constructed from Persian sentences and corresponding source fragments have been constructed from English sentences parallel with source sentences. To consider the degree of obfuscation in plagiarized fragments, a combination of sentences with different similarity score were chosen. The number of sentences and their similarity score in a fragment specifies the degree of obfuscation in that fragment. Different degrees of obfuscation are “Low”, “Medium”, and “High” obfuscation which is shown in Table 2. In this step, according to the length of suspicious document, one or more plagiarism cases which are in the same cluster of suspicious document are selected. Then, each of them is inserted at random positions in suspicious document. Persian documents considering as suspicious documents and source documents are English documents. Source fragments also, inserted at random positions in source documents. In other words, Persian translation of English fragments has been inserted into suspicious documents.
The fraction of plagiarism in each document is not a fixed value. The percentage of plagiarism in each suspicious document is distributed between 5% and 60% of its length. The ratio of plagiarism per suspicious documents is shown in Table 3.
Finally, for each pair of source and suspicious documents, an XML file was generated which contains meta information about the plagiarism cases. The metadata XML file includes: ─ this_length: Length of plagiarism case in the suspicious document. ─ this_offset: Start offset of the plagiarism case in the suspicious document. ─ source_reference: Name of source file. ─ source_length: Length of source fragment in source document. ─ source_offset: Start offset of the source fragment in the source document.
In this section, the statistics of our bilingual corpus are represented. An overview of important corpus statistics is shown in Table 4.
The established bilingual Persian-English plagiarism detection corpus is available at the website1 of “Research Institute for Information and Communication Technology” for research purposes. 5
In this paper we have described our approach to the task of text alignment corpus construction in the context of PAN 2015 competition. This corpus is intended to be used to evaluate the performance of bilingual plagiarism detection systems. We have exploited a sentence aligned parallel corpus to construct a bilingual Persian–English plagiarism detection corpus. Our main contribution is to use a novel obfuscation strategy by using the similarity scores between parallel sentences in such a way that the obfuscation degree can be adjusted in plagiarized passages. This corpus is the first bilingual plagiarism corpus for Persian language.
In the future works, we plan to improve our corpus by incorporating other obfuscation strategies such as manual obfuscation and artificial obfuscation in the corpus. We also plan to extend our corpus in other languages. 1 http://www.ictrc.ir/plaglab/corpora/Bilingual_Persian_English_Corpus(asghari15).zip This work has been accomplished in ICT research Institute, ACECR, under the support of Vice Presidency for Science and Technology of Iran - grant No. 1164331. The authors gratefully acknowledge the support of aforementioned organizations. Special thanks go to the members of ITBM research group for their valuable collaboration. The authors also would like to express their gratitude to Leila Tavakoli and Hamed Zamani.