阿拉伯语文本分类的机器学习:比较研究

D. Bouchiha, Abdelghani Bouziane, Noureddine Doumi
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引用次数: 1

摘要

机器学习(ML)的最终目标是让机器像人类一样行动。特别是,ML算法被广泛用于文本分类。文本分类是根据文本的内容将文本分类为预定义的类别集的过程。它有助于改进网络上的信息检索。在本文中,我们将重点放在“阿拉伯语”文本分类上,因为世界上有一个很大的社区使用这种语言。阿拉伯语文本分类过程包括预处理、特征提取和ML算法三个主要步骤。本文提出了一项比较实证研究,以了解哪种组合(特征提取- ML算法)在处理阿拉伯语文档时效果较好。因此,我们通过结合5种特征提取技术和32种机器学习算法实现了160个分类器。然后,为了AI和NLP社区的利益,我们将这些分类器开放访问。实验是使用一个巨大的开放数据集进行的。对比研究表明,TFIDF-Perceptron是一种性能最好的分类器组合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning for Arabic Text Classification: A Comparative Study
The ultimate aim of Machine Learning (ML) is to make machine acts like a human. In particular, ML algorithms are widely used to classify texts. Text classification is the process of classifying texts into a predefined set of categories based on the texts’ content. It contributes to improving information retrieval on the Web. In this paper, we focus on the "Arabic" text classification since there is a large community in the world that uses this language. The Arabic text classification process consists of three main steps: preprocessing, feature extraction and ML algorithm. This paper presents a comparative empirical study to see which combination (feature extraction - ML algorithm) acts well when dealing with Arabic documents. So, we implemented one hundred sixty classifiers by combining 5 feature extraction techniques and 32 machine learning algorithms. Then, we made these classifiers open access for the benefit of the AI and NLP communities. Experiments were carried out using a huge open dataset. The comparison study reveals that TFIDF-Perceptron is the best performing combination of a classifier.
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