使用优化机器学习管道的孟加拉语新闻标题分类

Prashengit Dhar, Md. Zainal Abedin
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引用次数: 5

摘要

:基于孟加拉文的新闻门户网站现在非常普遍,并且日益增加。随着互联网技术的普及,在线阅读新闻已成为一项常规任务。新闻门户中呈现不同类型的新闻。本文提出的系统对新闻门户网站的新闻标题进行分类。通过机器学习算法进行预测。对收集到的大量数据进行训练和测试。作为预处理任务,如标记化,数字去除,去除标点符号,符号和删除停止词进行处理。还手动创建了一组停止词。强烈的停止词会带来更好的表现。停止词删除在特征选择中起主导作用。在优化方面,采用遗传算法减小特征尺寸。并对无优化过程的比较进行了探讨。数据集是通过收集各种孟加拉语新闻门户网站的新闻标题而建立的。得到的输出具有良好的分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bengali News Headline Categorization Using Optimized Machine Learning Pipeline
: Bengali text based news portal is now very common and increasing day by day. With easy access of internet technology, reading news through online is now a regular task. Different types of news are represented in the news portal. The system presented in this paper categorizes the news headline of news portal or sites. Prediction is made by machine learning algorithm. Large number of collected data are trained and tested. As pre-processing tasks such as tokenization, digit removal, removing punctuation marks, symbols, and deletion of stop words are processed. A set of stop words is also created manually. Strong stop words leads to better performance. Stop words deletion plays a lead role in feature selection. For optimization, genetic algorithm is used which results in reduced feature size. A comparison is also explored without optimization process. Dataset is established by collecting news headline from various Bengali news portal and sites. Resultant output shows well performance in categorization .
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