Analyzing Semantic Concept Patterns to Detect Academic Plagiarism

Iraqi Journal of Science

Plagiarism is described as using someone else's ideas or work without their permission. Using lexical and semantic text similarity notions, this paper presents a plagiarism detection system for examining suspicious texts against available sources on the Web. The user can upload suspicious files in pdf or docx formats. The system will search three popular search engines for the source text (Google, Bing, and Yahoo) and try to identify the top five results for each search engine on the first retrieved page. The corpus is made up of the downloaded files and scraped web page text of the search engines' results. The corpus text and suspicious documents will then be encoded as vectors. For lexical plagiarism detection, the system will leverage Jaccard similarity and Term Frequency-Inverse Document Frequency (TFIDF) techniques, while for semantic plagiarism detection, Doc2Vec and Sentence Bidirectional Encoder Representations from Transformers (SBERT) intelligent text represen...

ArXiv, 2021

Identifying academic plagiarism is a pressing problem, among others, for research institutions, publishers, and funding organizations. Detection approaches proposed so far analyze lexical, syntactical, and semantic text similarity. These approaches find copied, moderately reworded, and literally translated text. However, reliably detecting disguised plagiarism, such as strong paraphrases, sense-for-sense translations, and the reuse of non-textual content and ideas, is an open research problem. The thesis addresses this problem by proposing plagiarism detection approaches that implement a different concept: analyzing non-textual content in academic documents, specifically citations, images, and mathematical content. To validate the effectiveness of the proposed detection approaches, the thesis presents five evaluations that use real cases of academic plagiarism and exploratory searches for unknown cases. The evaluation results show that non-textual content elements contain a high deg...

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 of digital documents seems a serious problem in today's era. Plagiarism refers to the use of someone's data, language and writing without proper acknowledgment of the original source. Plagiarism of another author's original work is one of the biggest problems in publishing, science, and education. Plagiarism can be of different types. This paper presents a different approach for measuring semantic similarity between words and their meanings. Existing systems are based on the traditional approach. For detecting plagiarism, traditional methods focus on text matching according to keywords but fail to detect intelligent plagiarism using semantic web. We have suggested new strategies for detecting the plagiarism in the user document using the semantic web. In paper we have proposed architecture and algorithms to better detection of copy case using semantic search, it can improve the performance of copy case detection system. It analyzes the user document. After the...

This paper proposes a hybrid approach to plagiarism detection in academic documents that integrates detection methods using citations, semantic argument structure, and semantic word similarity with character-based methods to achieve a higher detection performance for disguised plagiarism forms. Currently available software for plagiarism detection exclusively performs text string comparisons. These systems find copies, but fail to identify disguised plagiarism, such as paraphrases, translations, or idea plagiarism. Detection approaches that consider semantic similarity on word and sentence level exist and have consistently achieved higher detection accuracy for disguised plagiarism forms compared to character-based approaches. However, the high computational effort of these semantic approaches makes them infeasible for use in real-world plagiarism detection scenarios. The proposed hybrid approach uses citation-based methods as a preliminary heuristic to reduce the retrieval space with a relatively low loss in detection accuracy. This preliminary step can then be followed by a computationally more expensive semantic and character-based analysis. We show that such a hybrid approach allows semantic plagiarism detection to become feasible even on large collections for the first time.

2016

Abstract. We present a work on detection of manual paraphrasing in docu-ments in comparison with a set of source documents. Manual paraphrasing is a realistic type of plagiarism, where the obfuscation is introduced manually in documents. We have used PAN-PC-10 data set to develop and evaluate our al-gorithm. The proposed approach consists of two steps, namely, identification of probable plagiarized passages using dice similarity measure and filtering the obtained passages using syntactic rules and lexical semantic features extracted from obfuscation patterns. The algorithm works at sentence level. The results are encouraging in difficult cases of plagiarism that most of the existing ap-proaches fail to detect.

Plagiarism is the act of taking someone else writes and present as it owns. There are different types of plagiarism such as copy-paste, paraphrasing text, idea plagiarism, etc. From earlier paper, It seems that almost the tools based on ” vector space model”. This approach is suitable for identifying copy paste plagiarism. But it fails when words replaced by synonyms. Sentences can be paraphrased, conveying same meaning. Such sentences may easily pass a bag of word approach. Finding semantic is measure task to identify paraphrasing of words. Proposed method helps to find semantic of sentences using parts of speech of the sentences. This system helps to identify copy-paste plagiarism and finding plagiarized sentences that are semantically same but differ in syntax.

Expert Systems with Applications, 2015

Plagiarism is described as the reuse of someone else's previous ideas, work or even words without sufficient attribution to the source. This paper presents a method to detect external plagiarism using the integration of semantic relations between words and their syntactic composition. The problem with the available methods is that they fail to capture the meaning in comparison between a source document sentence and a suspicious document sentence, when two sentences have same surface text (the words are the same) or they are a paraphrase of each other. Therefore it causes inaccurate or unnecessary matching results. However, this method can improve the performance of plagiarism detection because it is able to avoid selecting the source text sentence whose similarity with suspicious text sentence is high but its meaning is different. It is executed by computing the semantic and syntactic similarity of the sentence-to-sentence. Besides, the proposed method expands the words in sentences to tackle the problem of information limit. It bridges the lexical gaps for semantically similar contexts that are expressed in a different wording. This method is also capable to identify various kinds of plagiarism such as the exact copied text, paraphrasing, transformation of sentences and changing of word structure in the sentences. As a result, the experimental results have displayed that the proposed method is able to improve the performance compared with the participating systems in PAN-PC-11. The experimental results also displayed that the proposed method demonstrates better performance as compared to other existing techniques on PAN-PC-10 and PAN-PC-11 datasets.

The problem of academic plagiarism has been present for centuries. Yet, the widespread dissemination of information technology, including the internet, made plagiarising much easier. Consequently, methods and systems aiding in the detection of plagiarism have attracted much research within the last two decades. Researchers proposed a variety of solutions, which we will review comprehensively in this article. Available detection systems use sophisticated and highly efficient character-based text comparisons, which can reliably identify verbatim and moderately disguised copies. Automatically detecting more strongly disguised plagiarism, such as paraphrases, translations or idea plagiarism, is the focus of current research. Proposed approaches for this task include intrinsic, cross-lingual and citation-based plagiarism detection. Each method offers unique strengths and weaknesses; however, none is currently mature enough for practical use. In the future, plagiarism detection systems may benefit from combining traditional character-based detection methods with these emerging detection approaches.

Journal of theoretical and applied information technology, 2011

Plagiarism is a form of academic misconduct. It has increased rapidly because it is now quick and easy to reach data and information through electronic documents and the Internet. The problem occurs when found documents content is illegal and without permission or citation, this problem is known as plagiarism. One of the major challenges is to detect the plagiarism and illegal copy. This paper discusses a new representation method for text documents called text graph-based representation. The proposed method does not represent the content of a text document as a graph only, but also captures the underlying semantic meaning in terms of the relationships among its concepts in order to defeat the difficulty which the traditional plagiarism detection systems face with some kinds of plagiarism such as complicated plagiarism in which users can reword the plagiarized part or replace some words by their synonyms. The experiments have been carried out using PAN-PC-09 standardization of plagi...