Text classification aims at mapping documents into a set of predefined categories.
Supervised machine learning models have shown great success in this area but they …

The traditional machine learning approaches for text classification often require labelled
data for learning classifiers. However, when applied to large-scale classification involving …

Supervised text classifiers require extensive human expertise and labeling efforts. In this
paper, we propose a weakly supervised text classification algorithm based on the labeling of …

Advanced pre-trained models for text representation have achieved state-of-the-art
performance on various text classification tasks. However, the discrepancy between the …

The increasing availability of digital documents in the last decade has prompted the
development of machine learning techniques to automatically classify and organize text …

We describe the results of extensive machine learning experiments on large collections of
Reuters' English and German newswires. The goal of these experiments was to …

A wide range of supervised learning algorithms has been applied to Text Categorization.
However, the supervised learning approaches have some problems. One of them is that …

Most existing methods for text categorization employ induction algorithms that use the words
appearing in the training documents as features. While they perform well in many …

This paper discusses a novel hybrid approach for text categorization that combines a
machine learning algorithm, which provides a base model trained with a labeled corpus …

Document categorization, which aims to assign a topic label to each document, plays a
fundamental role in a wide variety of applications. Despite the success of existing studies in …