当前位置: X-MOL 学术Enterp. Inf. Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Exploring deep learning approaches for Urdu text classification in product manufacturing
Enterprise Information Systems ( IF 4.4 ) Pub Date : 2020-05-05 , DOI: 10.1080/17517575.2020.1755455
Muhammad Pervez Akhter 1 , Zheng Jiangbin 1 , Irfan Raza Naqvi 1 , Mohammed Abdelmajeed 2 , Muhammad Fayyaz 3
Affiliation  

ABSTRACT

From last decade, machine learning (ML) techniques have been used for Urdu text processing. Due to lack of language resources, potential of deep learning (DL) models have not been exploited yet for Urdu text document classification. A text document has more noise, redundant information, and large vocabulary than short text like tweets. This study is the systematic comparison of four well-known DL models. We also compare DL models with four ML models. We also explore the various text preprocessing techniques. Experimental results show that CNN outperforms the others. Further, single-layer architecture of LSTM and BiLSTM performs better than multiple-layers architecture.



中文翻译:

探索产品制造中乌尔都语文本分类的深度学习方法

摘要

从过去十年开始,机器学习 (ML) 技术已被用于乌尔都语文本处理。由于缺乏语言资源,深度学习(DL)模型的潜力尚未被用于乌尔都语文本文档分类。与像推文这样的短文本相比,文本文档具有更多的噪音、冗余信息和大量词汇。本研究是对四种著名的深度学习模型的系统比较。我们还将 DL 模型与四个 ML 模型进行了比较。我们还探索了各种文本预处理技术。实验结果表明,CNN 优于其他。此外,LSTM 和 BiLSTM 的单层架构比多层架构表现更好。

更新日期:2020-05-05
down
wechat
bug