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 …

Recently, with the rapid developments of the Internet and social networks, there have been
tremendous increase in the amount of complex-structured text resources. These information …

Text classification is an important and classical problem in natural language processing.
Recently, Graph Neural Networks (GNNs) have been widely applied in text classification …

Feature selection (FS) constitutes a series of processes used to decide which relevant
features/attributes to include and which irrelevant features to exclude for predictive …

Covid text identification (CTI) is a crucial research concern in natural language processing
(NLP). Social and electronic media are simultaneously adding a large volume of Covid …

Text classification based on graph neural networks (GNNs) has been widely studied by
virtue of its potential to capture complex and across-granularity relations among texts of …

The paper presents quantum model of subjective text perception based on binary cognitive
distinctions corresponding to words of natural language. The result of perception is quantum …

This work shows the use of WEKA, a tool that implements the most common machine
learning algorithms, to perform a Text Mining analysis on a set of documents. Applying these …

This paper proposes a new algorithm for an automatic feature selection procedure in High
Dimensional Graphical Models. The algorithm, called Best-Path Algorithm (BPA), rests on a …

Increased advancement in a variety of study subjects and information technologies, has
increased the number of published research articles. However, researchers are facing …