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

A.M. Mutawa ^1,2* Ayshah Alrumaih ¹

• ¹ Computer Engineering Department, Kuwait University, Kuwait City, Kuwait
• ² Computer Sciences Department, University of Hamburg, Hamburg, Hamburg, Germany

The metrical structure of Classical Arabic poetry, deeply rooted in its rich literary heritage, is governed by 16 distinct meters, making its analysis both a linguistic and computational challenge. In this study, a deep learning-based approach was developed to accurately determine the meter of Arabic poetry using TensorFlow and a large dataset. Character-level encoding was employed to convert text into integers, enabling the classification of both full-verse and half-verse data. Notably, the data was evaluated without removing diacritics, preserving critical linguistic features. A traintest-split method with a 70-15-15 division was utilized, with 15% of the total dataset reserved as unseen test data for evaluation across all models. Multiple deep learning architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory (BiLSTM), were tested. Among these, the Bidirectional Long Short-Term Memory model achieved the highest accuracy, with 97.53% for full-verse and 95.23% for half-verse data. This study introduces an effective framework for Arabic meter classification, contributing significantly to the application of artificial intelligence in natural language processing and text analytics.