Cross-language Plagiarism Detection Research Articles

Word Embedding for High Performance Cross-Language Plagiarism Detection Techniques

Academic plagiarism has become a serious concern as it leads to the retardation of scientific progress and violation of intellectual property. In this context, we make a study aiming at the detection of cross-linguistic plagiarism based on Natural language Preprocessing (NLP), Embedding Techniques, and Deep Learning. Many systems have been developed to tackle this problem, and many rely on machine learning and deep learning methods. In this paper, we propose Cross-language Plagiarism Detection (CL-PD) method based on Doc2Vec embedding techniques and a Siamese Long Short-Term Memory (SLSTM) model. Embedding techniques help capture the text's contextual meaning and improve the CL-PD system's performance. To show the effectiveness of our method, we conducted a comparative study with other techniques such as GloVe, FastText, BERT, and Sen2Vec on a dataset combining PAN11, JRC-Acquis, Europarl, and Wikipedia. The experiments for the Spanish-English language pair show that Doc2Vec+SLSTM achieve the best results compared to other relevant models, with an accuracy of 99.81%, a precision of 99.75%, a recall of 99.88%, an f-score of 99.70%, and a very small loss in the test phase.

A Corpus for Evaluation of Cross Language Text Re-use Detection Systems

In recent years, the availability of documents through the Internet along with automatic translation systems have increased plagiarism, especially across languages. Cross-lingual plagiarism occurs when the source or original text is in one language and the plagiarized or re-used text is in another language. Various methods for automatic text re-use detection across languages have been developed whose objective is to assist human experts in analyzing documents for plagiarism cases. For evaluating the performance of these systems and algorithms, standard evaluation resources are needed. To construct cross lingual plagiarism detection corpora, the majority of earlier studies have paid attention to English and other European language pairs, and have less focused on low resource languages. In this paper, we investigate a method for constructing an English-Persian cross-language plagiarism detection corpus based on parallel bilingual sentences that artificially generate passages with various degrees of paraphrasing. The plagiarized passages are inserted into topically related English and Persian Wikipedia articles in order to have more realistic text documents. The proposed approach can be applied to other less-resourced languages. In order to evaluate the compiled corpus, both intrinsic and extrinsic evaluation methods were employed. So, the compiled corpus can be suitably included into an evaluation framework for assessing cross-language plagiarism detection systems. Our proposed corpus is free and publicly available for research purposes.

Cross-Language Plagiarism Detection: Methods, Tools, and Challenges: A Systematic Review

Plagiarism is one of the most serious academic offenses. However, people have adopted different approaches to avoid plagiarism, such as transcribing excerpts from one language. Thus, it is challenging to realize this plagiarism form unless someone fully understands another language. Researchers have developed approaches for detecting plagiarism in a variety of different languages. However, most methods created in the past have proved effective for detecting plagiarism in papers published in a single language, most notably English. Therefore, this paper aims to provide a systematic literature review of cross-language plagiarism detection methods (CLPD) in a natural language context. The approach used to perform this study consisted of an extensive search for relevant literature through an SLR and Snowballing. Therefore, we present an overview of (i) cross-language plagiarism detection techniques; (ii)the artifacts and the aspects that were considered in the evaluation phase; and(iii) the lack of guidelines and tools for its implementation. Its contribution lies in its ability to highlight emerging cross-language plagiarism detection techniques trends. Further, we identify any of these techniques in other domains, for instance, software engineering.

Meta-Analysis of Cross-Language Plagiarism and Self-Plagiarism Detection Methods for Russian-English Language Pair

Scientists need to publish the results of their work to remain demanded and stay relevant. The well-known principle of “publish or perish” often forces scientists to pursue an increase in quantity, not quality. Along with the problems of authorship, paid research, fabrication of results, plagiarism and self-plagiarism are among the most common violations. Their impact is more subtle, but no less destructive for the scientific community. Statistics shows that the reuse of texts in different languages is very common in various studies. Identification of translated plagiarism is a complex task, and there are almost no such tools on the Russian market now. In this article, we provided an overview of the existing methods for the identification of cross-language borrowings in the scientific articles of the authors. We did analysis of solutions by studying the works on various language pairs and paid the great attention to the Russian-English language pair.

Cross-language text alignment: A proposed two-level matching scheme for plagiarism detection

The exponential growth of documents in various languages throughout the web, along with the availability of several editing and translation tools have made the cross-language plagiarism detection a challenging issue. Regarding its high importance, the present study focuses on the task of cross-language text alignment also known as detailed analysis which works on the outputs of the source retrieval step of cross-language plagiarism detection systems. The paper proposes a two-level matching approach with the aim of considering both syntactic and semantic information to align plagiarism fragments from the source and suspicious documents, accurately. At the first level, a vector space model which employs a multilingual word embeddings based dictionary and a local weighting technique is used in order to extract a minimal set of highly potential candidate fragment pairs rather than considering all possible pairs of fragments. This step also contains a dynamic expansion technique to cover more candidate pairs aiming at improving the system’s recall. It is followed by a more precise algorithm that examines the candidate pairs at the sentence level using a graph-of-words representation of text. As a result, by modelling both the words and their relationships, an acceptable increase in the system’s precision which is the goal of the second level is also observed. To identify evidence of plagiarism, i.e. potential cases of unauthorized text reuse, the algorithm tries to find maximum cliques from the match graph of source and suspicious texts. With this two-level investigation, the approach is capable to discriminate true plagiarism cases from the original text. The experimental results on different datasets such as PAN-PC-11, PAN-PC-12, and SemEval-2017 show that the proposed cross-language text alignment approach significantly outperforms the state-of-the-art models and can be fed into an expert system for further improvement of cross-language plagiarism detection. The source codes are publicly available on GitHub11https://github.com/MeysamRoostaee/CLPD-DetailAnalysis/archive/master.zip, for the purposes of reproducible research.

Cross-Language Plagiarism Detection using Word Embedding and Inverse Document Frequency (IDF)

The purpose of cross-language textual similarity detection is to approximate the similarity of two textual units in different languages. This paper embeds the distributed representation of words in cross-language textual similarity detection using word embedding and IDF. The paper introduces a novel cross-language plagiarism detection approach constructed with the distributed representation of words in sentences. To improve the textual similarity of the approach, a novel method is used called CL-CTS-CBOW. Consequently, adding the syntax feature to the approach is improved by a novel method called CL-WES. Afterward, the approach is improved by the IDF weighting method. The corpora used in this study are four Arabic-English corpora, specifically books, Wikipedia, EAPCOUNT, and MultiUN, which have more than 10,017,106 sentences and uses with supported parallel and comparable assemblages. The proposed method in this paper combines different methods to confirm their complementarity. In the experiment, the proposed system obtains 88% English-Arabic similarity detection at the word level and 82.75% at the sentence level with various corpora.

Cross-Language Plagiarism Detection Based on CLAD Method

This paper describes the cross-language plagiarism detection method CLAD (Cross-Language Analog Detector) between test document and indexed documents. The main difference of this method from existing versions is the detection of plagiarism among multiple languages not only two languages. While translating terms, it used the dictionary-based machine-translation method. CLAD’s working process consists of document indexing and detection process phases. In this paper, we will describe both of these phases.

An effective approach to candidate retrieval for cross-language plagiarism detection: A fusion of conceptual and keyword-based schemes

Due to the rapid growth of documents and manuscripts in various languages all over the world, plagiarism detection has become a challenging task, especially for cross lingual cases. Because of this issue, in today's plagiarism detection systems, a candidate retrieval process is developed as the first step, in order to reduce the set of documents for comparison to a reasonable number. The performance of the second step of plagiarism detection, which is devoted to a detailed analysis of the candidates is tightly dependent on the candidate retrieval phase. Regarding its high importance, the present study focuses on the candidate retrieval task and aims to extract the minimal set of highly potential source documents, accurately. The paper proposes a fusion of concept-based and keyword-based retrieval models for this purpose. A dynamic interpolation factor is used in the proposed scheme in order to combine the results of conceptual and bag-of-words models. The effectiveness of the proposed model for cross language candidate retrieval is also compared with state-of-the-art models over German-English and Spanish-English language partitions. The results show that the proposed candidate retrieval model outperforms the state-of-the-art models and can be considered as a proper choice to be embedded in cross-language plagiarism detection systems.

Cross-lingual plagiarism detection techniques for English-Hindi language pairs

Plagiarism is defined as stealing of another author’s language, thoughts, or ideas as one’s own original work. Most of the work in the literature has been emphasized on monolingual plagiarism detection. Given text in two different languages, the objective of Cross-Lingual Plagiarism Detection (CLPD) model is to determine if the text in one language is plagiarized with the text written in another language. The cross-lingual plagiarism detection is mainly an unexplored area and is very important to improve the ethical practice, academic integrity and honesty in academics. The problem of CLPD has emerged as very important yet difficult problem because it is very easy to steal the content of a document written in another language through translation softwares. In this paper, we present different cross-language plagiarism detection approaches in context of Indian languages pairs such as Hindi-English, English-Hindi language pairs. The research in cross-language plagiarism detection is very limited but has opened up opportunities due to development in recent advancement in distributed word representation techniques in monolingual and bi-lingual context.

On the use of word embedding for cross language plagiarism detection

On the use of word embedding for cross language plagiarism detection

Arabic English Cross-Lingual Plagiarism Detection Based on Keyphrases Extraction, Monolingual and Machine Learning Approach

Due to rapid growth of research articles in various languages, cross-lingual plagiarism detection problem has received increasing interest in recent years. Cross-lingual plagiarism detection is more challenging task than monolingual plagiarism detection. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a method that combines keyphrases extraction, monolingual detection methods and machine learning approach. The research methodology used in this study has facilitated to accomplish the objectives in terms of designing, developing, and implementing an efficient Arabic – English cross lingual plagiarism detection.
 This paper empirically evaluates five different monolingual plagiarism detection methods namely i)N-Grams Similarity, ii)Longest Common Subsequence, iii)Dice Coefficient, iv)Fingerprint based Jaccard Similarity and v) Fingerprint based Containment Similarity. In addition, three machine learning approaches namely i) naïve Bayes, ii) Support Vector Machine, and iii) linear logistic regression classifiers are used for Arabic-English Cross-language plagiarism detection. Several experiments are conducted to evaluate the performance of the key phrases extraction methods. In addition, Several experiments to investigate the performance of machine learning techniques to find the best method for Arabic-English Cross-language plagiarism detection.
 According to the experiments of Arabic-English Cross-language plagiarism detection, the highest result was obtained using SVM classifier with 92% f-measure. In addition, the highest results were obtained by all classifiers are achieved, when most of the monolingual plagiarism detection methods are used.

Cross-Language Plagiarism Detection System Using Latent Semantic Analysis and Learning Vector Quantization

Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA) for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ) classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term–document matrix construction.

Fuzzy-Semantic Similarity for Automatic Multilingual Plagiarism Detection

A word may have multiple meanings or senses, it could be modeled by considering that words in a sentence have a fuzzy set that contains words with similar meaning, which make detecting plagiarism a hard task especially when dealing with semantic meaning, and even harder for cross language plagiarism detection. Arabic is known by its richness, word’s constructions and meanings diversity, hence changing texts from/to Arabic is a complex task, and therefore adopting a fuzzy semantic-based approach seems to be the best solution. In this paper, we propose a detailed fuzzy semantic-based similarity model for analyzing and comparing texts in CLP cases, in accordance with the WordNet lexical database, to detect plagiarism in documents translated from/to Arabic, a preprocessing phase is essential to form operable data for the fuzzy process. The proposed method was applied to two texts (Arabic/English), taking into consideration the specificities of the Arabic language. The result shows that the proposed method can detect 85% of the plagiarism cases.

Cross-language plagiarism detection over continuous-space- and knowledge graph-based representations of language

Cross-language (CL) plagiarism detection aims at detecting plagiarised fragments of text among documents in different languages. The main research question of this work is on whether knowledge graph representations and continuous space representations can complement to each other and improve the state-of-the-art performance in CL plagiarism detection methods. In this sense, we propose and evaluate hybrid models to assess the semantic similarity of two segments of text in different languages. The proposed hybrid models combine knowledge graph representations with continuous space representations aiming at exploiting their complementarity in capturing different aspects of cross-lingual similarity. We also present the continuous word alignment-based similarity analysis, a new model to estimate similarity between text fragments. We compare the aforementioned approaches with several state-of-the-art models in the task of CL plagiarism detection and study their performance in detecting different length and obfuscation types of plagiarism cases. We conduct experiments over Spanish-English and German-English datasets. Experimental results show that continuous representations allow the continuous word alignment-based similarity analysis model to obtain competitive results and the knowledge-based document similarity model to outperform the state-of-the-art in CL plagiarism detection.

Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information

The rapid growth of documents in different languages, the increased accessibility of electronic documents, and the availability of translation tools have caused cross-lingual plagiarism detection research area to receive increasing attention in recent years. The task of cross-language plagiarism detection entails two main steps: candidate retrieval and assessing pairwise document similarity. In this paper we examine candidate retrieval, where the goal is to find potential source documents of a suspicious text. Our proposed method for cross-language plagiarism detection is a keyword-focused approach. Since plagiarism usually happens in parts of the text, there is a requirement to segment the texts into fragments to detect local similarity. Therefore we propose a topic-based segmentation algorithm to convert the suspicious document to a set of related passages. After that, we use a proximity-based model to retrieve documents with the best matching passages. Experiments show promising results for this important phase of cross-language plagiarism detection.

Semantic Similarity/Relatedness for Cross Language Plagiarism Detection

<p>Generally utterances in natural language are highly ambiguous, and a unique interpretation can usually be determined only by taking into account the context in the utterance occurred. Automatically determining the correct sense of a polysemous word is a complicated problem especially in multilingual corpuses. This paper presents an application programming interface for several Semantic Relatedness/Similarity metrics measuring semantic similarity/distance between multilingual words and concepts, in order to use it after for sentences and paragraphs in Cross Language Plagiarism Detection (CLPD); using WordNet for the English-French and English-Arabic multilingual plagiarism cases.</p>

A systematic study of knowledge graph analysis for cross-language plagiarism detection

Cross-language plagiarism detection aims to detect plagiarised fragments of text among documents in different languages. In this paper, we perform a systematic examination of Cross-language Knowledge Graph Analysis; an approach that represents text fragments using knowledge graphs as a language independent content model. We analyse the contributions to cross-language plagiarism detection of the different aspects covered by knowledge graphs: word sense disambiguation, vocabulary expansion, and representation by similarities with a collection of concepts. In addition, we study both the relevance of concepts and their relations when detecting plagiarism. Finally, as a key component of the knowledge graph construction, we present a new weighting scheme of relations between concepts based on distributed representations of concepts. Experimental results in Spanish–English and German–English plagiarism detection show state-of-the-art performance and provide interesting insights on the use of knowledge graphs.

Arabic-English Cross-language Plagiarism Detection using Winnowing Algorithm

Arabic-English Cross-language Plagiarism Detection using Winnowing Algorithm

Multi Queries Methods of the Chinese-English Bilingual Plagiarism Detection

Cross-language plagiarism detection identifies and extracts plagiarized text in a multilingual environment. In recent years, there has been a significant amount of work done involving English and European text. However, somewhat less attention has been paid to Asia languages. We compared a number of different strategies for Chinese-English bilingual plagiarism detection. We present methods for candidate document retrieval and compare four methods: (i) document keywords based, (ii) intrinsic plagiarism based, (iii) headers based, and (iv) machine translation queries. The results of our evaluation indicated that keywords based queries, the simplest and most efficient approach, gives acceptable results for newspaper articles. We also compared different percentage of keywords based query, and the results indicated that putting 50% keywords into queries can obtain the satisfied candidate documents set.

Methods for cross-language plagiarism detection

Three reasons make plagiarism across languages to be on the rise: (i) speakers of under-resourced languages often consult documentation in a foreign language, (ii) people immersed in a foreign country can still consult material written in their native language, and (iii) people are often interested in writing in a language different to their native one. Most efforts for automatically detecting cross-language plagiarism depend on a preliminary translation, which is not always available.In this paper we propose a freely available architecture for plagiarism detection across languages covering the entire process: heuristic retrieval, detailed analysis, and post-processing. On top of this architecture we explore the suitability of three cross-language similarity estimation models: Cross-Language Alignment-based Similarity Analysis (CL-ASA), Cross-Language Character n-Grams (CL-CNG), and Translation plus Monolingual Analysis (T+MA); three inherently different models in nature and required resources.The three models are tested extensively under the same conditions on the different plagiarism detection sub-tasks—something never done before. The experiments show that T+MA produces the best results, closely followed by CL-ASA. Still CL-ASA obtains higher values of precision, an important factor in plagiarism detection when lesser user intervention is desired.