Due to its simplicity, efficiency, and effectiveness, multinomial naive Bayes (MNB) has been widely used for text classification. As in (NB), assumption of the conditional independence features is often violated and, therefore, reduces classification performance. Of numerous approaches alleviating features, structure extension attracted less attention from researchers. To best our knowledge, on...

Bu çalışmada makine öğrenmesi teknikleri ve konvolüsyonel sinir ağları (KSA) tabanlı bir derin öğrenme modeli kullanılarak iki farklı Türkçe metin veri kümesi sınıflandırılmıştır. Metin sınıflandırma çalışmasında Rastgele Orman (RO), Naive Bayes (NB), Destek Vektör Makineleri (DVM), K-En Yakın Komşu (KNN) Algoritmaları geliştirilen KSA seçilen kümelerine uygulanmıştır. dilinde kümeleri, sınıf a...

There are so many social networking sites available. Tweets have evolved into a crucial tool for gathering people's thoughts, ideas, behaviours and sentiments surrounding particular entities. One of the most intriguing subjects in this context is analyzing sentiment tweets using natural language processing (NLP). Although several methods been created, accuracy effectiveness those analysis yet t...

Text classification and text gradation are important for language teachers. Profiling readability studies examine textual linguistic features that determine difficulty. Arabic, as an under-resourced language, suffers from a lack of such which results in material developers textbook writers relying on their intuitions experiences selecting appropriate texts. At the classroom level, teachers ofte...

Pre-trained word embeddings encode general semantics and lexical regularities of natural language, have proven useful across many NLP tasks, including sense disambiguation, machine translation, sentiment analysis, to name a few. In supervised tasks such as multiclass text classification (the focus this article) it seems appealing enhance representations with ad-hoc that task-specific informatio...

Several text classification tasks such as sentiment analysis, news categorization, multi-label and opinion are challenging problems even for modern deep learning networks. Recently, Capsule Networks (CapsNets) proposed image classification. It has been shown that CapsNets have several advantages over Convolutional Neural (CNNs), while their validity in the domain of was less explored. In this p...

Multi-label text classification has been widely concerned by scholars due to its contribution practical applications. One of the key challenges in multi-label is how extract and leverage correlation among labels. However, it quite challenging directly model correlations labels a complex unknown label space. In this paper, we propose Label Prompt Text Classification (LP-MTC), which inspired idea...