Plagiarism Detection using Attention based Text Similarity
This paper focuses to solve two important problems faced in this digital era. The first problem is handling large amount of digital data. Digital sectors are finding difficulty in handling repetitive works and standardization of process. Our University is a eco-friendly, digital university each and every work of our students are maintained digitally. So for a course instructor to open, view, correct and finding plagiarism in the assignment or homework submitted by students is a tough task. So in this paper this tough task is simplified by applying Robotic Process Automation (RPA). The process of downloading and reading the students’ digital document is automated. The second problem is the plagiarism. There has been a massive threat in the academic and technological integrity of innovation in the name of paraphrasing. In a survey conducted by Donald McCabe of Rutgers University over 70 percent of students admit to paraphrasing and copying of others work. They have also fabricated bibliography. This problem can be addressed using the state-of-the-art document similarity deep learning neural networks. In this paper, we propose the design and implementation of a Siamese Neural Network (SNN), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to identify paraphrasing, using only features of similarity between words where the dataset is evolutionary and the model keeps learning with incremental sources of the document’s plagiarism. This paper will also provide the Tensorflow based implementation of such a model. The proposed system will predict the similarity between documents and predict the plagiarism percentage.
|Journal||Solid State Technology|