Linguistic and Statistical Traits Characterising Plagiarism (original) (raw)
Related papers
Automatic Detection of Plagiarism in Writing
Studies in Applied Linguistics and TESOL, 2022
This paper reports on preliminary steps to create an external plagiarism detection tool. I used the PAN-PC-11 data sets and extracted tf-idf scores of text documents and cosine similarity measures between source and suspicious documents to find text overlap. The model was able to successfully create vectors and measure the similarity metrics. However, the algorithm was not extended further to automatically retrieve related documents to follow on the pipeline (converting texts to n-grams for detailed analysis and revealing the best match as a source of plagiarism and evaluating the accuracy of the model). The model produced a matrix of cosine similarity for all the documents, which I used to manually retrieve documents and check for overlap using online tools. While extending the algorithm based on the suggested pipeline would allow for a more accurate evaluation of the model, manual comparison of sample documents provided some validity of the model developed for the present study.
Corpus and Evaluation Measures for Automatic Plagiarism Detection
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.
Expert Systems with Applications, 2013
Plagiarism detection is of special interest to educational institutions, and with the proliferation of digital documents on the Web the use of computational systems for such a task has become important. While traditional methods for automatic detection of plagiarism compute the similarity measures on a document-to-document basis, this is not always possible since the potential source documents are not always available. We do text mining, exploring the use of words as a linguistic feature for analyzing a document by modeling the writing style present in it. The main goal is to discover deviations in the style, looking for segments of the document that could have been written by another person. This can be considered as a classification problem using self-based information where paragraphs with significant deviations in style are treated as outliers. This so-called intrinsic plagiarism detection approach does not need comparison against possible sources at all, and our model relies only on the use of words, so it is not language specific. We demonstrate that this feature shows promise in this area, achieving reasonable results compared to benchmark models.
Review of Recent Plagiarism Detection Techniques and Their Performance Comparison
Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, 2020
With the explosive growth of technology and the easy availability of content on the web, it creates new challenges to discriminate against the original work from plagiarized material. Content is said to be plagiarized when it is taken from other original sources without giving its reference. To address this issue Plagiarism detection tools are required. Over the years, extensive work has been done in the development of anti-plagiarism tools. This paper presents the types of plagiarism with an aim to review Extrinsic Plagiarism detection techniques using Linguistic-based features, Syntactic-based features, and Semantic-based features. Further, an overview of some current state of art methodologies and their results has been discussed on the dataset of PAN-PC 2009, PAN-PC 2010, and PAN-PC 2011. This paper also analyzes the pros and cons of some existing systems and by comparing results it also identifies that some of the systems have less potency to detect the manual and highly shuffled complex types of plagiarism such as translation obfuscation. Keywords Plagiarism detection • Extrinsic plagiarism detection • Intrinsic plagiarism detection • PAN-PC datasets 1 Introduction World Wide Web provides access to data present in the documents, databases, and other sources of information using internet service. The availability of knowledge and information in the digital form leads to "Plagiarism" by "Plagiarist". Plagiarism
A Hybrid Algorithm for Identifying and Categorizing Plagiarised Text Documents
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...
Plagiarism: Taxonomy, Tools and Detection Techniques
ArXiv, 2018
To detect plagiarism of any form, it is essential to have broad knowledge of its possible forms and classes, and existence of various tools and systems for its detection. Based on impact or severity of damages, plagiarism may occur in an article or in any production in a number of ways. This survey presents a taxonomy of various plagiarism forms and include discussion on each of these forms. Over the years, a good number tools and techniques have been introduced to detect plagiarism. This paper highlights few promising methods for plagiarism detection based on machine learning techniques. We analyse the pros and cons of these methods and finally we highlight a list of issues and research challenges related to this evolving research problem.
A Novel Technique for Detecting Plagiarism in Documents Exploiting Information Sources
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 Detection Using Artificial Intelligence
International Journal of Computer and Information System (IJCIS), 2024
Presently available plagiarism detection technologies are primarily restricted to string-level comparisons between potentially original texts and suspiciously plagiarized materials. The objective of this research is to enhance the precision of plagiarism identification by integrating Natural Language Processing (NLP) methods into current methodologies. Our proposal is an external plagiarism detection framework that uses various natural language processing (NLP) approaches to examine a set of original and suspicious papers. The techniques not only analyze text strings but also the text's structure, taking text relations into consideration. Preliminary findings using a corpus of short paragraphs that have been plagiarized demonstrate that NLP approach increase the correctness of current methods.