Mutawa A M, Alrumaih Ayshah
Department of Computer Engineering, College of Engineering and Petroleum, Kuwait University, Safat, Kuwait.
Department of Computer Sciences, University of Hamburg, Hamburg, Germany.
Front Artif Intell. 2025 Feb 14;8:1523336. doi: 10.3389/frai.2025.1523336. eCollection 2025.
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. In particular, the data were evaluated without removing diacritics, preserving critical linguistic features. A train-test-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 (Bi-LSTM), 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.
古典阿拉伯诗歌的韵律结构深深植根于其丰富的文学遗产，由16种不同的韵律支配，这使得对其进行分析既是一项语言挑战，也是一项计算挑战。在本研究中，开发了一种基于深度学习的方法，使用TensorFlow和一个大型数据集来准确确定阿拉伯诗歌的韵律。采用字符级编码将文本转换为整数，从而能够对整行和半行数据进行分类。特别是，在评估数据时没有去除变音符，保留了关键的语言特征。使用了一种70-15-15划分的训练-测试分割方法，将总数据集的15%留作未见测试数据，用于评估所有模型。测试了多种深度学习架构，包括长短期记忆(LSTM)、门控循环单元(GRU)和双向长短期记忆(Bi-LSTM)。其中，双向长短期记忆模型的准确率最高，整行数据的准确率为97.53%，半行数据的准确率为95.23%。本研究为阿拉伯韵律分类引入了一个有效的框架，为人工智能在自然语言处理和文本分析中的应用做出了重要贡献。
