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Neural Network Based Bengali News Headline Multi Classification System: Selection of Features describes Comparative Performance

机译:基于神经网络的孟加拉新闻标题多分类系统:功能选择描述了比较效果

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The modern era is gradually developing in all sectors. Moreover, development is necessary to research. Accordingly natural language processing is the expanding area of process text. At present year, memorizing the data is very tough due to the rapidly growing volume of data. Newspapers are a great habit to the whole world of all ages. To acquire a variety of knowledge from different segments is entertaining by themselves. According to this work using bengali news headline may be more specific to others by defining its news type. Therefore the machine can smartly review the sequence of sentences within reach output to find newstype. With experiment, we connect our approach by Neural Network of adoption with 90% accuracy performance. Coming with a momentous outcome we've done Multi Classification reached at SVM, NB, Logistic Regression, Neural Network, Random forest applying bengali dataset.
机译:现代时代正在各个领域逐步发展。而且,发展对于研究是必要的。因此,自然语言处理是处理文本的扩展领域。当前,由于数据量的快速增长,记忆数据非常困难。报纸是各个年龄段的人的一个好习惯。从不同的领域获得各种知识是他们自己的娱乐。根据这项工作,通过定义孟加拉新闻头条的新闻类型,新闻头条可能对其他人更具体。因此,该机器可以智能地查看覆盖范围输出中的句子序列以查找新闻类型。通过实验,我们将采用神经网络的方法与90%的准确度性能联系起来。取得了重大成果,我们在孟加拉语数据集上实现了SVM,NB,Logistic回归,神经网络,随机森林等方面的多重分类。

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