Version 1
: Received: 3 March 2024 / Approved: 4 March 2024 / Online: 5 March 2024 (09:10:38 CET)
How to cite:
Mutawa, A.; Alrumaih, A. Determining the Meter of Classical Arabic Poetry Using Deep Learning: A Performance Analysis. Preprints2024, 2024030200. https://doi.org/10.20944/preprints202403.0200.v1
Mutawa, A.; Alrumaih, A. Determining the Meter of Classical Arabic Poetry Using Deep Learning: A Performance Analysis. Preprints 2024, 2024030200. https://doi.org/10.20944/preprints202403.0200.v1
Mutawa, A.; Alrumaih, A. Determining the Meter of Classical Arabic Poetry Using Deep Learning: A Performance Analysis. Preprints2024, 2024030200. https://doi.org/10.20944/preprints202403.0200.v1
APA Style
Mutawa, A., & Alrumaih, A. (2024). Determining the Meter of Classical Arabic Poetry Using Deep Learning: A Performance Analysis. Preprints. https://doi.org/10.20944/preprints202403.0200.v1
Chicago/Turabian Style
Mutawa, A. and Ayshah Alrumaih. 2024 "Determining the Meter of Classical Arabic Poetry Using Deep Learning: A Performance Analysis" Preprints. https://doi.org/10.20944/preprints202403.0200.v1
Abstract
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.
Keywords
Arabic poetry; Arabic meters; Bi-LSTM; Deep learning; Machine learning; Natural language processing
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
