In recent years, convolutional neural networks (CNNs) have gained considerable attention
in text classification because of the remarkable good performance they achieved in various …

This paper presents, TextConvoNet, a novel Convolutional Neural Network (CNN) based
architecture for binary and multi-class text classification problems. Most of the existing CNN …

One-hot CNN (convolutional neural network) has been shown to be effective for text
categorization (Johnson & Zhang, 2015). We view it as a special case of a general …

This article studies convolutional neural networks for Tigrinya (also referred to as Tigrigna),
which is a family of Semitic languages spoken in Eritrea and northern Ethiopia. Tigrinya is a …

Text classification is a foundational task in many NLP applications. Traditional text classifiers
often rely on many human-designed features, such as dictionaries, knowledge bases and …

Text classification is one of the fundamental tasks in the field of natural language
processing. The CNN-based approaches and RNN-based approaches have shown different …

Text classification is one of the research hotspots in the field of Natural Language
Processing (NLP). In this paper, TextCNN model based on Convolutional Neural Network …

Word embeddings act as an important component of deep models for providing input
features in downstream language tasks, such as sequence labelling and text classification …

Recurrent neural network (RNN) and convolutional neural network (CNN) are two prevailing
architectures used in text classification. Traditional approaches combine the strengths of …

Text classification is a pivotal task in NLP (Natural Language Processing), which has
received widespread attention recently. Most of the existing methods leverage the power of …