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 …

The use of word embedding models and deep learning algorithms are currently the most
common and popular trends to enhance the overall performance of a text classification …

In recent years, convolutional neural networks (CNNs) have gained considerable attention
in text classification because of the remarkable good performance they achieved in various …

In this bachelor thesis, I first introduce the machine learning methodology of text
classification with the goal to describe the functioning of neural networks. Then, I identify and …

SciELO - Brasil - An Experimental Analysis of Optimal Hybrid Word Embedding Methods for
Text Classification Using a Movie Review Dataset An Experimental Analysis of Optimal Hybrid …

Abstract Bag-of-ngrams (BoN) models are commonly used for representing text. One of the
main drawbacks of traditional BoN is the ignorance of n-gram's semantics. In this paper, we …

We propose an improved framework for document representation using word embeddings.
The existing models represent the document as a position vector in the same word …

In this paper, we conducted an experiment to build a classification model that combines
different techniques in most of the Natural Language Processing Tasks. We used the word …

Data representation is a fundamental task in machine learning, which affects the
performance of the whole machine learning system. In the past few years, with the rapid …

With the advancing growth of the World Wide Web (WWW) and the expanding availability of
electronic text documents, the automatic assignment of text classification (ATC) has become …