Arabic text mining based on clustering and coreference resolution

Mahmood, Salma A.; Alrufaye, Faiez Musa Lahmood

doi:10.1109/crcsit.2017.7965549

Cited by 3 publications

(1 citation statement)

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“…The highest clustering scores for the precision, recall, and F1-measure were 98%, 88%, and 93%, respectively. Mahmood and Al-Rufaye also addressed the problem of the high dimensionality of documents by minimizing the dimensionality of documents using the Term Frequency (TF), Inverse Document Frequency (IDF), and Term Frequency-Inverse Document Frequency (TF-IDF) feature selection approaches [22]. Following that, K-Means and K-Medoids were used for the clustering.…”

Section: Arabic Text Clusteringmentioning

confidence: 99%

Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization

Larabi-Marie-Sainte,

Bin Alamir,

Alameer

2023

Applied Sciences

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Arabic text clustering is an essential topic in Arabic Natural Language Processing (ANLP). Its significance resides in various applications, such as document indexing, categorization, user review analysis, and others. After inspecting the current work on clustering Arabic text, it is observed that most researchers focus on applying K-Means clustering while hindering other clustering techniques. Our evaluation shows that K-Means has a weakness of inconsistent clustering results and weak clustering performance when the data dimensionality increases. Unlike K-Means clustering, Artificial Neural Networks (ANN) models such as Self-Organizing Maps (SOM) demonstrated higher accuracy and efficiency in clustering even with high dimensional datasets. In this paper, we introduce a new clustering model based on an optimization technique called Grey Wolf Optimization (GWO) used conjointly with SOM clustering to enhance its clustering performance and accuracy. The evaluation results of our proposed technique show an improvement in the effectiveness and efficiency in comparison with state-of-the-art approaches.

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Section: Arabic Text Clusteringmentioning

confidence: 99%

Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization

Larabi-Marie-Sainte,

Bin Alamir,

Alameer

2023

Applied Sciences

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Neural Networks Algorithm for Arabic Language Features-Based Text Mining

Alrufaye

Mhaibes

Neamah

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Text mining aims to understand texts correctly by utilising several phases to collect those features of Arabic words which are valuable and important to the applications mentioned above in making a correct decision. The technology then builds a strong system that relies on AI techniques, such as neural networks, to collect words in accordance with those features. An ANN is a collection of connected nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron is one that receives a signal then processes it and can signal to neurons connected to it. The current study is concerned with building a system for analysing words in the Arabic language. This system can be included in any application to address the Arabic language, becoming part of it. The system generates strings for all names and pronouns appearing in the entered text and depends mainly on the automatic assembly of a set features by using neural networks. We implemented the system, with its two phases, on the documents in succession. The results were encouraging, ranging between 83% and 96%.

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Decision Tree Technique for Arabic Sentences Classification with Preprocessing of NLP by Using of Words Features

Alrufaye

Mhaibes

2022

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

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Arabic text mining based on clustering and coreference resolution

Cited by 3 publications

References 5 publications

Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization

Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization

Neural Networks Algorithm for Arabic Language Features-Based Text Mining

Decision Tree Technique for Arabic Sentences Classification with Preprocessing of NLP by Using of Words Features

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