Arabic Poetry Meter Categorization Using Machine Learning Based on Customized Feature Extraction

Alqasemi, Fahd; Al-Hagree, Salah; Abdu, Nail Adeeb Ali; Al-Helali, Baligh; Al-Gaphari, Ghaleb

doi:10.1109/itss-ioe53029.2021.9615302

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…The results show a 42% improvement in the error rate of diacritization. The study of [36] was based on machine learning algorithms and a diacritic text. An accuracy of 96.34% was achieved using support vector machines (SVM).…”

Section: Discussionmentioning

confidence: 99%

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Determining the Meter of Classical Arabic Poetry Using Deep Learning: A Performance Analysis

Mutawa,

Alrumaih

2024

Preprint

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Classical Arabic poetry is a form of Arabic literature, and almost every piece of Arabic poetry follows one of the 16 meters. The rhythmic framework of the poems or the verses is called meters. The deep learning (DL) model was implemented using TensorFlow in this work using a large dataset of Arabic poetry. The character level encoding was used to convert text to integers for classifying the full-verse and half-verse data. The study evaluates the data without removing diacritics from the text. The train-test-split method with a 70-15-15 split was employed for classification. 15% of the total data was considered unseen test data for all models. The work was conducted with multiple deep learning models, including the Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bidirectional LSTM (Bi-LSTM). The Bi-LSTM model shows the best accuracy of all the models specified, with 97.53% for full-verse and 95.23% for half-verse without removing diacritic data.

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Section: Discussionmentioning

confidence: 99%

“…Many natural language processing (NLP) sequences-to-sequence methods use LSTM, GRU, BiLSTM, and BiGRU deep learning models [28,[30][31][32]. ML is also used in many cases like dialect detection, poetry classification, text classification, and sentiment analysis [17,[33][34][35][36].…”

Section: Literature Reviewmentioning

confidence: 99%

Determining the Meter of Classical Arabic Poetry Using Deep Learning: A Performance Analysis

Mutawa,

Alrumaih

2024

Preprint

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“…What Guyard goes to, is a repeated investment in the quantitative structure to determine the places of the stress, and this makes these places of rhythmic effectiveness related to the syllabic quantity of the meters in Arabic poetry. (Alqasemi et al, 2021;Consoli, 2023) The entirety of what is formulated about exporting the theory that governs the dynamics of rhythm in Arabic poetry is a subject to an impressionistic rooting that does not respond to the phenomena surrounding the meters of Al-Khalil. This has been revealed by the failure to adapt the slips and the defects (Zuhafat and Ilal) to the conventions of relativity and proportionality on which the rhythm is based.…”

Section: Introductionmentioning

confidence: 99%

The Arabic Poetry Of Al-Tafi’ila: Between The Terminology And the Rhythmic Determinants

Alomari

2024

j. arabic learning

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This study aims at clarifying the rhythmeic values of the ancient and modern meters of Arabic poetry as a basic tool in the Arabic language teaching/learning process. It seeks to enable the students to compare and contrast the Arabic rhythm in terms of 'Tafi’ila'. It is based on pursuing the facts of the connection and the separation between the deliberative prosodic studies’ description of the nature of the rhythm that governs what is called the term 'Al-Tafi’ila poetry'. In addition, the path of the actual verification of this rhythm in modern Arabic poetry, which is done through the systematic investment in Al-Khalil’s theory to examine the rhythmic basis of "Al-Tafi’ila poetry". It is found that this examination of the theory of traditional prosody allows to remove the ambiguity about the claims of the radical modernization brought about by the “Al-Tafi’ila” system on the contemporary Arabic poem. It will also establish a comprehensive conception of the reliability of the term "Al-Tafi’ila poetry" in representing the rhythmic nature of the poems that belong to it.

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Integrating K-Means Clustering and Levenshtein Distance and K-Nearest Neighbor Algorithms for Enhanced Arabic Sentiment Analysis

Al-Gaphari,

AL-Hagree,

Alsayadi

2024

Lecture Notes on Data Engineering and Communications Technologies

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Arabic Poetry Meter Categorization Using Machine Learning Based on Customized Feature Extraction

Cited by 8 publications

References 11 publications

Determining the Meter of Classical Arabic Poetry Using Deep Learning: A Performance Analysis

Determining the Meter of Classical Arabic Poetry Using Deep Learning: A Performance Analysis

The Arabic Poetry Of Al-Tafi’ila: Between The Terminology And the Rhythmic Determinants

Integrating K-Means Clustering and Levenshtein Distance and K-Nearest Neighbor Algorithms for Enhanced Arabic Sentiment Analysis

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