External Plagiarism Detection Research Articles

Design and Implementation of a Final Project Plagiarism Detection System Using Cosine Similarity Method

Plagiarism is an act of taking ideas, taking research results, acquiring research results, and summarizing writing without mentioning the source, either intentionally or unintentionally. The cosine similarity method can be used to calculate the score of similarities between documents by comparing existing documents on the database with uploaded ones to gain a similarity percentage. External plagiarism detection (EPD) is used to compare the contents of articles. The design and implementation of the system will be carried out in several stages in the hope that the system can work optimally and detect text similarities accurately. This research objective is to check the plagiarism rate of the document using the cosine similarity method as a method of calculating word equations called Kipcheck. The purpose of checking the level of plagiarism is to ensure that the documents created have a minimum level of fraud to avoid academic sanctions. Kipcheck uses three application system tests; maximum word calculation that can be processed; comparing application and manual calculation; and testing the consistency of application calculation results based on two different schemes.

An external plagiarism detection system based on part-of-speech (POS) tag n-grams and word embedding

The aim of this paper is to present an automatic plagiarism detection system to identify plagiarized passages of documents. Our plagiarism detection system uses both syntactic and semantic similarities to identify plagiarized passages. Our proposed method is a novel contribution because of its usage of part-of-speech tag n-grams (POSNG) which are able to show syntactic similarities between source and suspicious sentences. Each source document is indexed according to part-of-speech (POS) tag n-grams by a search engine in order to access rapidly to sentences that are possible plagiarism candidates. Even though our plagiarism detection system obtains very good results just using POS tag n-grams, its performance is further improved with the usage of semantic similarities. The semantic relatedness between words is measured with the word embedding technique called Word2Vec and the longest common subsequence approach is used to measure the semantic similarity between source and suspicious sentences. There are several types of plagiarism such as verbatim, paraphrasing, source-code, and cross-lingual. The high obfuscation paraphrasing is a type of plagiarism and its detection is one of the most difficult plagiarism detection tasks. Our proposed method, which is based on POS tag n-grams, improves the detection performance of the high obfuscation paraphrasing type and is the main contribution of this paper. For this study, we use the large dataset called PAN-PC-11 which is created for the evaluation of automatic plagiarism detection algorithms. Our experiments are conducted with the four types of paraphrasing in PAN-PC-11 which are none, low, high and simulated obfuscation paraphrasing types. We defined various threshold and parameter settings in order to assess the diversity of our results. We compared the performance of our method with the plagiarism detectors in the 3rd International Competition on Plagiarism Detection (PAN11). According to the experimental results, the proposed method achieved the best performance in terms of plagdet measure in the types of high and low obfuscation paraphrasing and produced competitive results in the other paraphrasing types.

Automatic Detection of Plagiarism in Writing

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.

Plagiarism Detection Algorithm Model Based on NLP Technology

We can bear in mind that each of us has plagiarized a text without realizing that it was plagiarism, Plagiarism can happen in Articles, Papers, Researches, literature, music, software, scientific, newspapers, websites, Master and PHD Thesis and many other fields, So plagiarism has become serious major problem to teachers, researchers and publishers, There are divergent opinions about how to define plagiarism and what makes plagiarism serious. So, the detecting plagiarism is very important, so in this survey we explicate the concept of ;plagiarism ; and provide an overview of different plagiarism software and tools to solve the plagiarism problem, and will discuss the plagiarism process, types and detection methodologies. We can define that plagiarism is the brief and the description of this sentence ;someone used someone else’s mental product (such as its texts, ideas, or privacy). We suggest that what makes plagiarism so reprehensible is that it distorts scientific credit. In addition, intentional plagiarism indicates dishonesty. Moreover, there are a number of possible negative consequences of plagiarism. So we just create a framework for external plagiarism detection in which a some NLP processes are applied to process a set of suspicious and original documents, we have classified the different plagiarism detection techniques based on Lexical, Semantic, Syntactic and grammar analysis algorithms, And all of these algorithms precedes it NLP processing.

Analysis of Stylometric Features and Segmentation Strategies in Intrinsic Plagiarism Detection System

Two different paradigms in the field of plagiarism detection resulting in External Plagiarism Detection (EPD) and Intrinsic Plagiarism Detection (IPD) systems. The most common applied system is EPD, which requires its algorithm to make a heuristic comparison between a suspicious document with documents in a corpus. In contrast, given a suspicious document only, an algorithm of IPD should be able to find the plagiarism section by looking for text segments having different writing styles. Previous researches for Indonesian texts fell only in the field of the EPD development system. Therefore, this research focuses on and contributes to experimenting and analyzing the stylometric features and segmentation strategies to build an IPD system for Indonesian texts. The experimentation results show that the paragraph segment performs better by scoring 0.92 for Macro Averaged-Accuracy and 0.54 for Macro Averaged-F1. The stylometric features achieving the highest scores of F-1 and Accuracy are the frequency of punctuation, the average paragraph length, and the type-token ratio.

Similarity Detection in Large Volume Data using Machine Learning Techniques

When unauthorized copying or stealing of intellectual properties of others happen, it is called plagiarism. Two main approaches are used to counter this problem – external plagiarism detection and intrinsic plagiarism detection. External algorithms compare a suspicious file with numerous sources whereas intrinsic algorithms are allowed to solely inspect the suspicious file in order to predict plagiarism. In this work, the area chosen for detecting plagiarism is with programs or source code files. Copying the entire source code or logic used in a particular program without permissions or copyright is the stealing that happens in the case of source codes. There exist many ways to detect plagiarism in source code files. To perform plagiarism checking for a large dataset, the computational cost is very high and moreover it’s a time consuming job. To achieve a computationally efficient similarity detection in source code files, the Hadoop framework is used where parallel computation is possible for large datasets. But the raw data available to us is not in a suitable form for the existing plagiarism checking tools to work with, as their size is too high and they possess features of big data. Thus a qualifying model is required for the dataset, to be fed into Hadoop so that it could efficiently process them to check for plagiarism in source codes. To generate such a model, machine learning is used which incorporates big data with machine learning.

External Plagiarism Detection based on Human Behaviors in Producing Paraphrases of Sentences in English and Persian Languages

With the advent of the internet and easy access to digital libraries, plagiarism has become a major issue. Applying search engines is one of the plagiarism detection techniques that converts plagiarism patterns to search queries. Generating suitable queries is the heart of this technique and existing methods suffer from lack of producing accurate queries, Precision and Speed of retrieved results. This research proposes a framework called ParaMaker. It generates accurate paraphrases of any sentence, similar to human behaviors and sends them to a search engine to find the plagiarism patterns. For English language, ParaMaker was examined against six known methods with standard PAN2014 datasets. Results showed an improvement of 34% in terms of Recall parameter while Precision and Speed parameters were maintained. In Persian language, statements of suspicious documents were examined compared to an exact search approach. ParaMaker showed an improvement of at least 42% while Precision and Speed were maintained.

Latent Dirichlet Allocation and POS Tags Based Method for External Plagiarism Detection

In this article we present a new semantic and syntactic-based method for external plagiarism detection. In the proposed approach, latent dirichlet allocation (LDA) and parts of speech (POS) tags are used together to detect plagiarism between the sample and a number of source documents. The basic hypothesis is that considering semantic and syntactic information between two text documents may improve the performance of the plagiarism detection task. Our method is based on two steps, naming, which is a pre-processing where we detect the topics from the sentences in documents using the LDA and convert each sentence in POS tags array; then a post processing step where the suspicious cases are verified purely on the basis of semantic rules. For two types of external plagiarism (copy and random obfuscation), we empirically compare our approach to the state-of-the-art N-gram based and stop-word N-gram based methods and observe significant improvements.

Minmax Circular Sector Arc for External Plagiarism’s Heuristic Retrieval stage

Heuristic Retrieval (HR) task aims to retrieve a set of documents from which the External Plagiarism detection identifies plagiarized pieces of text. In this context, we present Minmax Circular Sector Arcs (MinmaxCSA) algorithms that treats HR task as an approximate k-nearest neighbor search problem. Moreover, MinmaxCSA algorithms aim to retrieve the set of documents with greater amounts of plagiarized fragments, while reducing the amount of time to accomplish the HR task. Our theoretical framework is based on two aspects: (i) a triangular property to encode a range of sketches on a unique value; and (ii) a Circular Sector Arc property which enables (i) to be more accurate. Both properties were proposed for handling high-dimensional spaces, hashing them to a lower number of hash values. Our two MinmaxCSA methods, Minmax Circular Sector Arcs Lower Bound (CSAL) and Minmax Circular Sector Arcs Full Bound (CSA), achieved Recall levels slightly more imprecise than Minmaxwise hashing in exchange for a better Speedup in document indexing and query extraction and retrieval time in high-dimensional plagiarism-related datasets.

A linguistic treatment for automatic external plagiarism detection

Plagiarism is the unauthorized use of the ideas, presentation of someone else's words or work as your own. This paper presents an External Plagiarism Detection System (EPDS), which employs a combination of the Semantic Role Labeling (SRL) technique, the semantic and syntactic information. Most of the available methods fail to capture the meaning in the comparison between a source document sentence and a suspicious document sentence when two sentences have same surface text. Therefore, it leads to incorrect or even unnecessary matching results. However, the proposed method is able to avoid selecting the source text sentence whose similarity with suspicious text sentence is high but its meaning is different. On the other hand, an author may change the sentence from: active to passive and vice versa; hence, the method also employed the SRL technique to tackle the aforementioned challenge. Furthermore, the method used the content word expansion approach to bridge the lexical gaps and identify the similar ideas that are expressed using different wording. The proposed method is able to detect different types of plagiarism such as the exact verbatim copying, paraphrasing, transformation of sentences, changing of word structure. 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 and other existing techniques.

Vocabulary Richness Metric for Extracting Author’s Semantic Mark in English Written Literary Works

(ProQuest: ... denotes formulae omitted.)1 IntroductionIntrinsic plagiarism detection implies the recognition of those parts of text within a document that are different taking into account the writing style of a certain author. Those parts are later on analysed as input data for the verification using external plagiarism detection tools. If a document in written by a single author, it is supposed that the passages written by him to be similar accordingly to its unique writing style.Using this technique of comparing the writing style within each part of text from the papers written by multiple authors and adding unsupervised automatic classification techniques, those parts of text are grouped in clusters depending on the membership of each author. The problem of plagiarism detection using this type of analysis involves extracting the unique writing style of each author, method also called stylometry analysis. Having a set of characteristics that best describes in a unique manner the writing style of an author, a metric is created for value description of percentage membership of documents to authors. In the research conducted in [3], [4], [5], [6], [7], [8] and [10], the problems and methods of inserting intrinsic plagiarism are referred, adding into the discussion also the stylometry, the writing style of a specific author over his history of research or just within a single document.Regardless of the type of plagiarism evaluation, intrinsic or external one, it is very important to determine the set of characteristics that must be taken into account in order to obtain as accurate results as possible. Those characteristics depend on the set of analysed documents, the language in which the documents are written and also the type of documents. The present research paper addresses the problem of literary English written documents by English native or European authors. For extracting from the initial set of documents the semantic analysis that describes the stylometry, multidimensional analysis is used. Chapter 2 reveals the relation between semantic analysis and main vocabulary richness metrics used in order to extract a value indicator of the words found in the analysed authors' set of written documents, transformed into tokens, and the semantic distances between them. The terms of words, tokens and frequency appearance are presented along with the main set of features of written style. The pre-processing phase is also presented, a step needed to convert words into WordNet tokens.In chapter 3, the improved semantic richness vocabulary metric is presented and defined along with an example of applying in upon a given phrase. The time evolution analyses is done within chapter 4, where 13 years values are inserted into a time series. Using three methods, absolute mean change, average index and linear regression, the trend indicator is evaluated. Comparing the sum of squared errors of the three methods, the linear regression method is chosen for the forecast. The conclusions are withdrawn in chapter 5 along with the future work directions.2Vocabulary Richness Metrics In Stilometry AnalysisFor analysis of an author's style of writing in the context of external analysis or intrinsic characteristics of plagiarism, the richness of vocabulary is defined as the characteristic of the author defines the degree to which the author uses words in a wider or narrower vocabulary. This feature was demonstrated in works such as [1], [2], as a feature closely related to the author, it can be fed into optimal set of features of the style of writing.Table 1 contains a list of metrics used to assess vocabulary wealth within the set of features writing, detailing the variables in formulas defined metrics that are presented in this paper [1].where:N - total number of words in the document analyzed;V - total concepts identified in the set of words;V^sub i^ - total concepts that appear of i times in the document;p^sub v^ - the relative frequency of the most v present concept in the document. …