Arabic Language Modeling Based on Supervised Machine Learning

Mahmoudi, Omayma; Bouami, Mouncef Filali; Badri, Mustapha

doi:10.18280/ria.360315

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…In some literature, it is explained that data preprocessing is a must for processing text data in the field of natural language processing. At the preprocessing stage [50], the text will carry out the process stages such as case folding which changes the letters in the data to lowercase, tokenizing aims to separate each word in the data, stopword aims to collect words that are not important and deletes data and stemming aims to make changes to words that affixes become standard words or basic words after that the data is analyzed manually to see the word structure if there are still words that are not good then manual annotations will be made to ensure the data is structured then the data will be divided into 2 columns, namely the text data column and the summary column data with the aim of implementing the T5 model and optimizing parameters with Bayesian optimization.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Enhancing Text Summarization with a T5 Model and Bayesian Optimization

Lubis,

Safitri,

Irvan

et al. 2023

RIA

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At present the habits and interests of individuals in obtaining information by reading large amounts of information have changed at the stage of reading information more concisely, but these changes have challenges such as the nature of the data which is still unstructured making it difficult to summarize text. This study applies a data cleaning process with text processing and manually annotates to divide the data into summary data and text data so that it can be used for the process of implementing the T5 model and Bayesian optimization. In the implementation of Bayesian optimization using the prior distribution and likelihood parameters. In implementing the T5 model there will be several stages such as processing training and test data then Decodification and Post-Processing processes. The results of this study were obtained using the ROUGE evaluation technique which resulted in an increased evaluation value. The T5 model produces a ROUGE 1 value with an average value of 0.42, ROUGE-2 has a value of 0.55 and ROUGE-L has a value of 0.46 while applying Bayesian optimization produces a ROUGE-1 evaluation with an average value of 0.53 ROUGE-2 has a value of 0.55 and ROUGE-L has a value of 0.59.

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Section: Data Acquisition and Processingmentioning

confidence: 99%

Enhancing Text Summarization with a T5 Model and Bayesian Optimization

Lubis,

Safitri,

Irvan

et al. 2023

RIA

View full text Add to dashboard Cite

show abstract

“…In particular, the proposal of a bidirectional encoder representations from transformers (BERT) model has brought revolutionary progress to NLP tasks [12][13][14][15]. Performance has been significantly improved in various NLP tasks using BERT-based pre-training models, such as generative pre-trained transformer (GPT) series of models [16][17][18].…”

Section: Students' Composition Evaluation Model Based On a Natural La...mentioning

confidence: 99%

Students’ Composition Evaluation Model Based on Natural Language Processing Algorithm

Wang¹,

Deng²

2023

Int. J. Emerg. Technol. Learn.

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It is subjective, time consuming and labor intensive to evaluate students’ compositions. Use of natural language processing (NLP) technology effectively improves the evaluation efficiency and reduces the burden on teachers. In order to overcome the problems of traditional models, such as over-fitting and poor generalization ability, this research studied a students’ composition evaluation model based on an NLP algorithm. A students’ composition evaluation model based on a multi-task learning framework was proposed, which completed three sub-tasks simultaneously using the NLP algorithm. Three different encoding methods were used; namely, convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), which captured text information from multiple perspectives. A new pairing pre-training mode was built, which aimed to help build an NLP-based students’ composition evaluation model based on the multi-task learning framework, thus alleviating the deviation caused by excessive correlation. The experimental results verified that the constructed model and the proposed method were effective.

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Arabic Speech Emotion Recognition Using Deep Neural Network

Mahmoudi

Bouami

2023

Lecture Notes in Networks and Systems

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Arabic Language Modeling Based on Supervised Machine Learning

Cited by 8 publications

References 21 publications

Enhancing Text Summarization with a T5 Model and Bayesian Optimization

Enhancing Text Summarization with a T5 Model and Bayesian Optimization

Students’ Composition Evaluation Model Based on Natural Language Processing Algorithm

Arabic Speech Emotion Recognition Using Deep Neural Network

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