Plagiarism Alignment Detection by Merging Context Seeds

Neural computing & applications (Print), 2019

The efficiency and scalability of plagiarism detection systems have become a major challenge due to the vast amount of available textual data in several languages over the Internet. Plagiarism occurs in different levels of obfuscation, ranging from the exact copy of original materials to text summarization. Consequently, designed algorithms to detect plagiarism should be robust to the diverse languages and different type of obfuscation in plagiarism cases. In this paper, we employ text embedding vectors to compare similarity among documents to detect plagiarism. Word vectors are combined by a simple aggregation function to represent a text document. This representation comprises semantic and syntactic information of the text and leads to efficient text alignment among suspicious and original documents. By comparing representations of sentences in source and suspicious documents, pair sentences with the highest similarity are considered as the candidates or seeds of plagiarism cases. To filter and merge these seeds, a set of parameters, including Jaccard similarity and merging threshold, are tuned by two different approaches: offline tuning and online tuning. The offline method, which is used as the benchmark, regulates a unique set of parameters for all types of plagiarism by several trials on the training corpus. Experiments show improvements in performance by considering obfuscation type during threshold tuning. In this regard, our proposed online approach uses two statistical methods to filter outlier candidates automatically by their scale of obfuscation. By employing the online tuning approach, no distinct training dataset is required to train the system. We applied our proposed method on available datasets in English, Persian and Arabic languages on the text alignment task to evaluate the robustness of the proposed methods from the language perspective as well. As our experimental results confirm, our efficient approach can achieve considerable performance on the different datasets in various languages. Our online threshold tuning approach without any training datasets works as well as, or even in some cases better than, the training-base method.

2015

Advancement in internet technology has made information resources more readily available and much easier for plagiarism to be carried out. Detecting plagiarism is by no means a trivial task because of the sophisticated tactics by which plagiarist disguise their sources. In this paper we present a hybrid algorithm for identifying and categorizing plagiarised text documents. We built our algorithm by combining the potentials of three standard textual similarity measures used in information retrieval (IR). We used the back propagation neural network (BPNN) for combining the measures and the PAN@Clef 2012 text alignment corpus for experimental purpose. We experimented with four categories of plagiarism with each category representing a degree of textual similarity. We measured performance in terms of precision, recall and fmeasure. Comparative analysis using the same corpus revealed that our hybrid algorithm (HA) outperformed each of the base similarity measures (BSM) in detecting three...

2014

Text alignment is a sub-task in the plagiarism detection process. In this paper we discuss our approach to address this problem. Our approach is based on mapping text alignment to the problem of subsequence matching just as previous works. We have prepared a framework, which lets us combine different feature types and different strategies for merging the features. We have proposed two different solutions to relax the comparison of two documents, so as to consider the semantic relations between them. Our first approach is based on defining a new feature type that contains semantic information about its corresponding doc- ument. In our second approach we have proposed a new method for comparing the features considering their semantic relations. Finally, We have applied DB- SCAN clustering algorithm to merge features in a neighborhood in both source and suspicious documents. Our experiments indicate that different feature sets are suitable for detecting different types of plagiarism.

Cognitive Computation, 2017

Plagiarism takes place when we use any person's work without giving due acknowledgment. There are several fields where the text similarity is involved like web document retrieval, information mining, and searching related articles. Several approaches have been introduced for detecting plagiarism in the text documents based on the syntactic structure of the text, string similarity, fingerprinting, semantic meaning underlying the text, etc. The basic limitation of plagiarism detection systems these days is that they fail to detect tough cases of plagiarism. The proposed plagiarism detection approach is the hybrid of semantic and syntactic similarity between the text documents. This novel approach exploits linguistic information sources non-linearly using the lexical database for finding the relatedness between text documents. The proposed approach uses semantic knowledge to perform cognitive-inspired computing. The framework is capable of detecting intelligent plagiarism cases like a verbatim copy, paraphrasing, rewording in a sentence, and sentence transformation. The approach has been evaluated on the standard PAN-PC-11 dataset. The experiments show that our technique has outperformed other strong baseline techniques in terms of precision, recall, F-measure, and plagiarism detection (PlagDet) score.

Plagiarism has always been a concern in many sectors, particularly in education. With the sharp rise in the number of electronic resources available online, an increasing number of plagiarism cases has been observed in recent years. As the amount of source materials is vast, the use of plagiarism detection tools has become the norm to aid the investigation of possible plagiarism cases. This paper describes an approach to improve plagiarism detection by incorporating a lexical generalisation technique. The goal is to identify plagiarised texts even if they are paraphrased using different words. Experiments performed on a subset of the PAN"10 corpus show that the matching approach involving lexical generalisation yields promising results, as compared to standard n-gram matching strategies.

In this paper, we describe the algorithm that has been used to carry out our plagiarism detection within the context of PAN10 competition. Our system is based on the LempelZiv distance, which is applied to extract structural information from texts. Then the algorithm tries to find outliers in the vector of distances between each fragment of the text and the whole document itself.

2010

The simple access to texts on digital libraries and the WWW has led to an increased number of plagiarism cases in recent years, which renders manual plagiarism detection infeasible at large. Various methods for automatic plagiarism detection have been developed whose objective is to assist human experts to analyze documents for plagiarism. Unlike other tasks in natural language processing and information retrieval, it is not possible to publish a collection of real plagiarism cases for evaluation purposes since they cannot be properly anonymized. Therefore, current evaluations found in the literature are incomparable and often not even reproducible. Our contribution in this respect is a newly developed large-scale corpus of artificial plagiarism and new detection performance measures tailored to the evaluation of plagiarism detection algorithms.

uni-weimar.de

This paper reports the best performed approach followed for the candidate document retrieval task and the approach used for the detailed comparison task of the Plagiarism detection track in PAN 2012. The aim of the participation was to understand a few of the computer-assisted approaches used for plagiarism detection. The plagiarism detection is dependent on two broad tasks, (1) the candidate document retrieval task and (2) the detailed comparison task. The N-gram similarity overlap metric was used for candidate document retrieval task and the greedy string tiling algorithm for detailed comparison task. The evaluation results suggested that the approach used for the candidate document retrieval task was highly competitive, but the approach used for detailed comparison task need much more improvement.

Information Fusion, 2015

Plagiarism refers to the act of presenting external words, thoughts, or ideas as one's own, without providing references to the sources from which they were taken. The exponential growth of different digital document sources available on the Web has facilitated the spread of this practice, making the accurate detection of it a crucial task for educational institutions. In this article, we present DOCODE 3.0, a Web system for educational institutions that performs automatic analysis of large quantities of digital documents in relation to their degree of originality. Since plagiarism is a complex problem, frequently tackled at different levels, our system applies algorithms in order to perform an information fusion process from multi data source to all these levels. These algorithms have been successfully tested in the scientific community in solving tasks like the identification of plagiarized passages and the retrieval of source candidates from the Web, among other multi data sources as digital libraries, and have proven to be very effective. We integrate these algorithms into a multi-tier, robust and scalable JEE architecture, allowing many different types of clients with different requirements to consume our services. For users, DOCODE produces a number of visualizations and reports from the different outputs to let teachers and professors gain insights on the originality of the documents they review, allowing them to discover, understand and handle possible plagiarism cases and making it easier and much faster to analyze a vast number of documents. Our experience here is so far focused on the Chilean situation and the Spanish language, offering solutions to Chilean educational institutions in any of their preferred Virtual Learning Environments. However, DOCODE can easily be adapted to increase language coverage.

2011

Plagiarism detection has been considered as a classification problem which can be approximated with intrinsic strategies, considering self-based information from a given document, and external strategies, considering comparison techniques between a suspicious document and different sources. In this work, both intrinsic and external approaches for plagiarism detection are presented. First, the main contribution for intrinsic plagiarism detection is associated to the outlier detection approach for detecting changes in the author's style. Then, the main contribution for the proposed external plagiarism detection is the space reduction technique to reduce the complexity of this plagiarism detection task. Results shows that our approach is highly competitive with respect to the leading research teams in plagiarism detection.