Feature extraction in the field of Text Processing or Natural Language Processing (NLP) has
its own challenges due to the characteristics of unstructured text. Thus, the selection of the …

Efficient word representation techniques (word embeddings) with modern machine learning
models have shown reasonable improvement on automatic text classification tasks …

The rapid increment in internet usage has also resulted in bulk gerenation of text data.
Therefore, investigation of new techniques for automatic classification of textual content is …

Today, extreme amounts of data are produced, and this is commonly referred to as Big Data.
A significant amount of big data is composed of textual data, and as such, text processing …

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 …

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 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 …

The word embedding model word2vec tends to ignore the importance of a single word to the
entire document, which affects the accuracy of the news text classification method. To …

The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …

In this paper, we examine the results of applying three different text feature extraction
approaches while classifying short sentences and phrases into categories with a neural …