Text classification (TC) is the task of automatically assigning documents to a fixed number of
categories. TC is an important component in many text applications. Many of these …

The most prevalent representation for text classification is the bag-of-words vector. A number
of approaches have sought to replace or augment the bag-of-words representation with …

In this article we propose a data treatment strategy to generate new discriminative features,
called compound-features (or c-features), for the sake of text classification. These c-features …

Preprocessing is one of the key components in a typical text classification framework. This
paper aims to extensively examine the impact of preprocessing on text classification in terms …

With the advent of the modern pre-trained Transformers, the text preprocessing has started
to be neglected and not specifically addressed in recent NLP literature. However, both from …

Text Classification pipelines are a sequence of tasks needed to be performed to classify
documents into a set of predefined categories. The pre-processing phase (before training) of …

This paper presents a formal analysis of popular text classification methods, focusing on
their loss functions whose minimization is essential to the optimization of those methods …

Most learning algorithms that are applied to text categorization problems rely on a bag-of-
words document representation, ie, each word occurring in the document is considered as a …

Traditionally, text classifiers are built from labeled training examples. Labeling is usually
done manually by human experts (or the users), which is a labor intensive and time …

The rapidly developing internet and other media have produced a tremendous amount of
text data, making it a challenging and valuable task to find a more effective way to analyze …