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DOI: 10.14569/IJACSA.2019.0101185
PDF

Rich Style Embedding for Intrinsic Plagiarism Detection

Author 1: Oumaima Hourrane
Author 2: El Habib Benlahmer

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 11, 2019.

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Abstract: Stylometry plays an important role in the intrinsic plagiarism detection, where the goal is to identify potential plagiarism by analyzing a document involving undeclared changes in writing style. The purpose of this paper is to study the interaction between syntactic structures, attention mechanism, and contextualized word embeddings, as well as their effectiveness on plagiarism detection. Accordingly, we propose a new style embedding that combines syntactic trees and the pre-trained Multi-Task Deep Neural Network (MT-DNN). Additionally, we use attention mechanisms to sum the embeddings, thereby exper-imenting with both a Bidirectional Long Short-Term Memory (BiLSTM) and a Convolutional Neural Network (CNN) max-pooling for sentences encoding. Our model is evaluated on two sub-task; style change detection and style breach detection, and compared with two baseline detectors based on classic stylometric features.

Keywords: Plagiarism detection; style embedding; deep neural network; stylometry; syntactic trees

Oumaima Hourrane and El Habib Benlahmer, “Rich Style Embedding for Intrinsic Plagiarism Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 10(11), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101185

@article{Hourrane2019,
title = {Rich Style Embedding for Intrinsic Plagiarism Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101185},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101185},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {11},
author = {Oumaima Hourrane and El Habib Benlahmer}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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