用深度学习自动划分阿拉伯文献历史时期，覆盖从古至今的文本。
Automatic Classification of Arabic Literature into Historical Eras
- 基于神经网络构建文本分类模型，识别阿拉伯语文学的时代特征。
- 二元分类最高F1达0.83，15类分类仍保持0.20的可辨识性。
- 适用于语言演化研究、数字人文及跨时代文本分析者。
阿拉伯语随时间经历了显著演变，包括新词出现、旧词消亡及词汇使用变化，这在古典与现代阿拉伯语之间尤为明显。尽管历史学家和语言学家已将阿拉伯文学划分为多个时期，但针对非诗歌文本的自动年代分类研究仍较匮乏。本文采用神经网络与深度学习技术，实现阿拉伯文本在不同历史时期的自动分类。模型在两个公开语料库（OpenITI 和 APCD）构建的数据集上评估，涵盖前伊斯兰至现代时期的文本。实验涵盖从二分类到15类分类的任务，既考虑预设历史分期，也探索自定义周期划分。结果表明，在二分类任务中，OpenITI数据集上F1得分为0.83，APCD数据集上为0.79；在15类分类任务中，OpenITI数据集上F1为0.20，12类任务中APCD数据集为0.18。
原文摘要 · Abstract (English)
The Arabic language has undergone notable transformations over time, including the emergence of new vocabulary, the obsolescence of others, and shifts in word usage. This evolution is evident in the distinction between the classical and modern Arabic eras. Although historians and linguists have partitioned Arabic literature into multiple eras, relatively little research has explored the automatic classification of Arabic texts by time period, particularly beyond the domain of poetry. This paper addresses this gap by employing neural networks and deep learning techniques to automatically classify Arabic texts into distinct eras and periods. The proposed models are evaluated using two datasets derived from two publicly available corpora, covering texts from the pre-Islamic to the modern era. The study examines class setups ranging from binary to 15-class classification and considers both predefined historical eras and custom periodizations. Results range from F1-scores of 0.83 and 0.79 on the binary-era classification task using the OpenITI and APCD datasets, respectively, to 0.20 on the 15-era classification task using OpenITI and 0.18 on the 12-era classification task using APCD.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。