One of the crucial problems of natural language processing for languages such as Ukrainian
is lack of datasets both unlabeled (for pretraining of word embeddings or large deep …

The use of ensemble learning, deep learning, and effective document representation
methods is currently some of the most common trends to improve the overall accuracy of a …

Neural bag-of-words models (NBOW) have achieved great success in text classification.
They compute a sentence or document representation by mathematical operations such as …

Text classification has a growing interest among NLP researchers due to its tremendous
availability on online platforms and emergence on various Web 2.0 applications. Recently …

Since the evolution of digital and online text content, automatic document classification has
become a significant research issue. There is a most commonly used machine learning …

In the last decade, online content has entered a stage where news related organizations are
reluctant to invest in offline operations due to excessive aberrations in content distributions …

In this tutorial we introduce three approaches to preprocess text data with Natural Language
Processing (NLP) and perform text document classification using machine learning. The first …

In recent years, Natural Language Processing (NLP) and text analysis has been a topic of
great interest among researchers. The utilization of deep learning techniques such as LSTM …

Text classification is one of the important fields in Natural Language Processing (NLP). It
assigns text documents into at least two categories in the domain by submitting and deriving …

The text represents one of the most widespread sequential models and as such is well
suited to the application of deep learning models from sequential data. Deep learning …