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Classical Arabic Poetry: Classification based on Era

机译:古典阿拉伯语诗:基于时代的分类

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This paper proposes a CNN-based deep learning model that classifies Arabic poems based on its era, which is not reported before. To build this model, constructing a dataset is the first step, so we propose an updated Arabic Poetry Dataset (2020). We use FastText word embeddings, based on the full corpus of poems (unlabeled). Two classifiers were trained, namely, a supervised deep learning classifier and a FastText-based classifier. We conducted several experiments. First, we implemented a polarity classifier of poems to modern and non-modern eras, which achieved highest accuracy and F1-score of 0.913 and 0.914, respectively, using a deep learning model without frequent terms. In the second experiment, we categorized poems into three eras. The classifier reported an accuracy and F1-score of 0.875 each. Last, the classification of poems into five different eras achieved highest accuracy and F1-score of 0.801 and 0.796, respectively.
机译:本文提出了基于CNN的深度学习模型,根据其时代对阿拉伯语诗进行分类,这是之前未报告的。要构建此模型,构建数据集是第一步,因此我们提出了一个更新的阿拉伯语诗歌数据集(2020)。我们使用FastText Word Embeddings,基于诗歌的完整语料(未标记)。培训了两个分类器,即监督的深层学习分类器和基于FastText的分类器。我们进行了几个实验。首先,我们实施了现代和非现代时代的诗歌的极性分类器,其分别在没有经常术语的情况下使用深度学习模型的最高精度和F1分数为0.913和0.914。在第二个实验中,我们将诗分类为三个时代。分类器报告了精度,F1分数为0.875。最后,诗分为五种不同的时代的典型达到了最高的精度和F1分数分别为0.801和0.796。

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