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arXiv 2003.01345cs.CLcs.LG
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基于机器和深度学习的乌尔都语文本文档分类方法的基准性能

Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification

Muhammad Nabeel Asim, Muhammad Usman Ghani, Muhammad Ali Ibrahim, Sheraz Ahmad, Waqar Mahmood, Andreas Dengel

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摘要：

为了提供基准性能的乌尔都语文本文档分类，本文的贡献是多方面的。首先，它提供了一个公开可用的基准数据集，手动标记为6个类。其次，它研究了传统的基于机器学习的乌尔都语文本文档分类方法的性能影响，嵌入10个过滤器为基础的特征选择算法，已被广泛用于其他语言。第三，第一次评估了各种基于深度学习的乌尔都语文本文档分类方法的性能。在这方面，为了进行实验，我们采用了10种深度学习分类方法，这些方法为英语文本分类提供了最佳性能数据。第四，它还研究了迁移学习的性能影响，利用双向编码器表示Transformers方法乌尔都语。第五，它评估了一种混合方法的完整性，该方法结合了传统的基于机器学习的特征工程和基于深度学习的自动特征工程。实验结果表明，在两个闭源基准数据集CLE Urdu Digest 1000 k和CLE Urdu Digest 1Million上，归一化差异度量和支持向量机的特征选择方法分别以32%和13%的显著性裕度表现出最先进的性能。在所有三个数据集上，归一化差异度量优于其他基于过滤器的特征选择算法，因为它显著提升了所有采用的机器学习，深度学习和混合方法的性能。源代码和呈现的数据集可在Github存储库中获得。

英文摘要：

In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it pro-vides a publicly available benchmark dataset manually tagged against 6 classes. Second, it investigates the performance impact of traditional machine learning based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages. Third, for the very first time, it as-sesses the performance of various deep learning based methodologies for Urdu text document classification. In this regard, for experimentation, we adapt 10 deep learning classification methodologies which have pro-duced best performance figures for English text classification. Fourth, it also investigates the performance impact of transfer learning by utiliz-ing Bidirectional Encoder Representations from Transformers approach for Urdu language. Fifth, it evaluates the integrity of a hybrid approach which combines traditional machine learning based feature engineering and deep learning based automated feature engineering. Experimental results show that feature selection approach named as Normalised Dif-ference Measure along with Support Vector Machine outshines state-of-the-art performance on two closed source benchmark datasets CLE Urdu Digest 1000k, and CLE Urdu Digest 1Million with a significant margin of 32%, and 13% respectively. Across all three datasets, Normalised Differ-ence Measure outperforms other filter based feature selection algorithms as it significantly uplifts the performance of all adopted machine learning, deep learning, and hybrid approaches. The source code and presented dataset are available at Github repository.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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