Poetry is an important component of any language. Much of a nation’s history and culture are documented in poems. A poem has a rhythmic flow which is quite different as compared to a prose. Each language has its own set of rhythmical structures for poems, called meters. Identifying the meters of Arabic poems is a lengthy and complicated process. To classify a poem’s meter, the text of the poem should be encoded in a special Arudi form which needs complex rule-based transformations before another set of rules can be used to finally classify the meters. This paper introduces a novel method for classifying poem meters of Arabic poems using RNN-based deep learning. It bypasses the need to transform the poem to the Arudi form as well as the need to explicitly encode the complex rules that are usually followed to determine the meter. The presented method was evaluated on a large dataset collected specifically for this purpose. We are able to classify the poem meters with an accuracy of 94.32% on an independent test set.

诗歌是任何语言的重要组成部分。一首诗记录了一个国家的许多历史和文化。一首诗的节奏流与散文相比有很大不同。每种语言都有一套自己的诗歌节奏结构，称为米。识别阿拉伯诗歌的韵律是一个漫长而复杂的过程。要对一首诗的计量表进行分类，应该以一种特殊的Arudi形式对诗歌文本进行编码，这需要基于规则的复杂转换，然后才能使用另一套规则对这些计量表进行最终分类。本文介绍了一种基于RNN的深度学习对阿拉伯诗歌的诗表进行分类的新方法。无需将诗转变为阿鲁迪形式以及明确编码通常用来确定仪表的复杂规则的需求。在专门为此目的收集的大型数据集上评估了所提出的方法。在独立的测试仪上，我们能够以94.32％的准确度对诗歌表进行分类。