In recent years, there has been an exponential growth in the number of
complex documents and texts that require a deeper understanding of machine
learning methods to be able to accurately classify texts in many applications.
Many machine learning approaches have achieved surpassing results in natural
language processing. The success of these learning algorithms relies on their
capacity to understand complex models and non-linear relationships within data.
However, finding suitable structures, architectures, and techniques for text
classification is a challenge for researchers. In this paper, a brief overview
of text classification algorithms is discussed. This overview covers different
text feature extractions, dimensionality reduction methods, existing algorithms
and techniques, and evaluations methods. Finally, the limitations of each
technique and their application in the real-world problem are discussed.
%0 Generic
%1 kowsari2019classification
%A Kowsari, Kamran
%A Meimandi, Kiana Jafari
%A Heidarysafa, Mojtaba
%A Mendu, Sanjana
%A Barnes, Laura E.
%A Brown, Donald E.
%D 2019
%K Mypaper
%R 10.3390/info10040150
%T Text Classification Algorithms: A Survey
%U http://arxiv.org/abs/1904.08067
%X In recent years, there has been an exponential growth in the number of
complex documents and texts that require a deeper understanding of machine
learning methods to be able to accurately classify texts in many applications.
Many machine learning approaches have achieved surpassing results in natural
language processing. The success of these learning algorithms relies on their
capacity to understand complex models and non-linear relationships within data.
However, finding suitable structures, architectures, and techniques for text
classification is a challenge for researchers. In this paper, a brief overview
of text classification algorithms is discussed. This overview covers different
text feature extractions, dimensionality reduction methods, existing algorithms
and techniques, and evaluations methods. Finally, the limitations of each
technique and their application in the real-world problem are discussed.
@misc{kowsari2019classification,
abstract = {In recent years, there has been an exponential growth in the number of
complex documents and texts that require a deeper understanding of machine
learning methods to be able to accurately classify texts in many applications.
Many machine learning approaches have achieved surpassing results in natural
language processing. The success of these learning algorithms relies on their
capacity to understand complex models and non-linear relationships within data.
However, finding suitable structures, architectures, and techniques for text
classification is a challenge for researchers. In this paper, a brief overview
of text classification algorithms is discussed. This overview covers different
text feature extractions, dimensionality reduction methods, existing algorithms
and techniques, and evaluations methods. Finally, the limitations of each
technique and their application in the real-world problem are discussed.},
added-at = {2019-06-11T10:24:17.000+0200},
author = {Kowsari, Kamran and Meimandi, Kiana Jafari and Heidarysafa, Mojtaba and Mendu, Sanjana and Barnes, Laura E. and Brown, Donald E.},
biburl = {https://www.bibsonomy.org/bibtex/29dbc379143bea3265f667755071b9e0a/kk7nc},
description = {Text Classification Algorithms: A Survey},
doi = {10.3390/info10040150},
interhash = {ccff93d9531c8680e29aa85e4f56d656},
intrahash = {9dbc379143bea3265f667755071b9e0a},
keywords = {Mypaper},
note = {cite arxiv:1904.08067},
timestamp = {2019-06-11T10:24:17.000+0200},
title = {Text Classification Algorithms: A Survey},
url = {http://arxiv.org/abs/1904.08067},
year = 2019
}