Sentence Embedding and Convolutional Neural Network for Semantic Textual Similarity Detection in Arabic Language

Mahmoud, Adnen; Zrigui, Mounir

doi:10.1007/s13369-019-04039-7

Cited by 28 publications

(18 citation statements)

References 29 publications

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“…Taking the painting style of Xin'an School as an example, this algorithm is used to convert the sample image into a new image of Xin'an School. Reference [6] proposed the Cycle GANSN algorithm. After the Cycle GAN algorithm identifies each convolutional layer of the network, a spectral normalization layer is added, the spectral norm of the convolutional layer parameter matrix is estimated by the power iteration method, and the stochastic gradient descent method is used to update the convolutional layer parameters.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Remote Judgment Method of Painting Image Style Plagiarism Based on Wireless Network Multitask Learning

Wang

2021

Wireless Communications and Mobile Computing

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Since the artistry of the work cannot be accurately described, the identification of reproducible plagiarism is more difficult. The identification of reproducible plagiarism of digital image works requires in-depth research on the artistry of artistic works. In this paper, a remote judgment method for plagiarism of painting image style based on wireless network multitask learning is proposed. According to this new method, the uncertainty of painting image samples is removed based on multitask learning algorithm edge sampling. The deep-level details of the painting image are extracted through the multitask classification kernel function, and most of the pixels in the image are eliminated. When the clustering density is greater than the judgment threshold, it can be considered that the two images have spatial consistency. It can also be judged based on this that the two images are similar, that is, there is plagiarism in the painting. The experimental results show that the discrimination rate is always close to 100%, the misjudgment rate of plagiarism of painting images has been reduced, and the various indicators in the discrimination process are the lowest, which fully shows that a very satisfactory discrimination result can be obtained.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Remote Judgment Method of Painting Image Style Plagiarism Based on Wireless Network Multitask Learning

Wang

2021

Wireless Communications and Mobile Computing

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“…Therefore, there is a need for another method to identify the participants’ language-based groups no matter what they wrote about to the discussion board. For example, Mahmoud et al [ 47 ] used natural language processing techniques to identify how similar two written documents, which can be modified to group the comments.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Discussion-Based Cross-Domain Performance Prediction of MOOC Learners Grouped by Language on FutureLearn

et al. 2021

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“…Second, hybrid feature-based approach that uses lexical, semantic and syntactic-semantic knowledge using Jaccard coefficient, Cosine similarity, and Lexical Markup Framework (LMF) standardized dictionaries ( Wali, Gargouri & Hamadou, 2017 ). Lastly, word embedding using IDF and Part-of-Speech (POS) tagging weighting methods ( El Moatez Billah Nagoudi, 2017 ) and deep learning using Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM) Mahmoud & Zrigui (2019) . However, all these works have not studied the effect of stemming on semantic text similarity.…”

Section: Related Workmentioning

confidence: 99%

Effect of stemming on text similarity for Arabic language at sentence level

Alhawarat

Abdeljaber

Hilal

2021

PeerJ Computer Science

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Semantic Text Similarity (STS) has several and important applications in the field of Natural Language Processing (NLP). The Aim of this study is to investigate the effect of stemming on text similarity for Arabic language at sentence level. Several Arabic light and heavy stemmers as well as lemmatization algorithms are used in this study, with a total of 10 algorithms. Standard training and testing data sets are used from SemEval-2017 international workshop for Task 1, Track 1 Arabic (ar–ar). Different features are selected to study the effect of stemming on text similarity based on different similarity measures. Traditional machine learning algorithms are used such as Support Vector Machines (SVM), Stochastic Gradient Descent (SGD) and Naïve Bayesian (NB). Compared to the original text, using the stemmed and lemmatized documents in experiments achieve enhanced Pearson correlation results. The best results attained when using Arabic light Stemmer (ARLSTem) and Farasa light stemmers, Farasa and Qalsadi Lemmatizers and Tashaphyne heavy stemmer. The best enhancement was about 7.34% in Pearson correlation. In general, stemming considerably improves the performance of sentence text similarly for Arabic language. However, some stemmers make results worse than those for original text; they are Khoja heavy stemmer and AlKhalil light stemmer.

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Sentence Embedding and Convolutional Neural Network for Semantic Textual Similarity Detection in Arabic Language

Cited by 28 publications

References 29 publications

[Retracted] Remote Judgment Method of Painting Image Style Plagiarism Based on Wireless Network Multitask Learning

[Retracted] Remote Judgment Method of Painting Image Style Plagiarism Based on Wireless Network Multitask Learning

Deep Learning for Discussion-Based Cross-Domain Performance Prediction of MOOC Learners Grouped by Language on FutureLearn

Effect of stemming on text similarity for Arabic language at sentence level

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