Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks

Ilyos Rabbimov; Sami Kobilov; Iosif Mporas

doi:10.1109/AICT50176.2020.9368822

Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks

Ilyos Rabbimov, Sami Kobilov, Iosif Mporas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The rapid growth of online news belonging to different categories is causing users to spend a lot of time and effort searching for relevant and important news. Text categorization has a great significance in information retrieval and natural language processing where unstructured text can be organized into predefined categories. In this paper we investigate Uzbek news categorization using a convolution neural network and four word embedding models. We obtain two new word embeddings for Uzbek and present them in the Uzbek news categorization task.

Original language	English
Title of host publication	2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	5
ISBN (Electronic)	9781728173863
ISBN (Print)	9781728173870
DOIs	https://doi.org/10.1109/AICT50176.2020.9368822
Publication status	Published - 9 Mar 2021
Event	The 14th IEEE International Conference on Application of Information and Communication Technologies - Tashkent, Uzbekistan Duration: 7 Oct 2020 → 9 Oct 2020 http://www.aict.info/2020

Publication series

Name	14th IEEE International Conference on Application of Information and Communication Technologies, AICT 2020 - Proceedings

Conference

Conference	The 14th IEEE International Conference on Application of Information and Communication Technologies
Abbreviated title	AICT 2020
Country/Territory	Uzbekistan
City	Tashkent
Period	7/10/20 → 9/10/20
Internet address	http://www.aict.info/2020

Keywords

CNN
Uzbek
multi-class classification
news categorization
word embeddings

Access to Document

10.1109/AICT50176.2020.9368822Licence: Unspecified

Cite this

Rabbimov, I., Kobilov, S., & Mporas, I. (2021). Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks. In 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Proceedings (14th IEEE International Conference on Application of Information and Communication Technologies, AICT 2020 - Proceedings). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/AICT50176.2020.9368822

Rabbimov, Ilyos ; Kobilov, Sami ; Mporas, Iosif. / Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks. 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2021. (14th IEEE International Conference on Application of Information and Communication Technologies, AICT 2020 - Proceedings).

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title = "Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks",

abstract = "The rapid growth of online news belonging to different categories is causing users to spend a lot of time and effort searching for relevant and important news. Text categorization has a great significance in information retrieval and natural language processing where unstructured text can be organized into predefined categories. In this paper we investigate Uzbek news categorization using a convolution neural network and four word embedding models. We obtain two new word embeddings for Uzbek and present them in the Uzbek news categorization task.",

keywords = "CNN, Uzbek, multi-class classification, news categorization, word embeddings",

author = "Ilyos Rabbimov and Sami Kobilov and Iosif Mporas",

note = "{\textcopyright} 2020 IEEE. ; The 14th IEEE International Conference on Application of Information and Communication Technologies, AICT 2020 ; Conference date: 07-10-2020 Through 09-10-2020",

year = "2021",

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doi = "10.1109/AICT50176.2020.9368822",

language = "English",

isbn = "9781728173870",

series = "14th IEEE International Conference on Application of Information and Communication Technologies, AICT 2020 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

booktitle = "2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Proceedings",

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Rabbimov, I, Kobilov, S & Mporas, I 2021, Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks. in 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Proceedings. 14th IEEE International Conference on Application of Information and Communication Technologies, AICT 2020 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), The 14th IEEE International Conference on Application of Information and Communication Technologies, Tashkent, Uzbekistan, 7/10/20. https://doi.org/10.1109/AICT50176.2020.9368822

Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks. / Rabbimov, Ilyos; Kobilov, Sami; Mporas, Iosif.
2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2021. (14th IEEE International Conference on Application of Information and Communication Technologies, AICT 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks

AU - Rabbimov, Ilyos

AU - Kobilov, Sami

AU - Mporas, Iosif

PY - 2021/3/9

Y1 - 2021/3/9

N2 - The rapid growth of online news belonging to different categories is causing users to spend a lot of time and effort searching for relevant and important news. Text categorization has a great significance in information retrieval and natural language processing where unstructured text can be organized into predefined categories. In this paper we investigate Uzbek news categorization using a convolution neural network and four word embedding models. We obtain two new word embeddings for Uzbek and present them in the Uzbek news categorization task.

AB - The rapid growth of online news belonging to different categories is causing users to spend a lot of time and effort searching for relevant and important news. Text categorization has a great significance in information retrieval and natural language processing where unstructured text can be organized into predefined categories. In this paper we investigate Uzbek news categorization using a convolution neural network and four word embedding models. We obtain two new word embeddings for Uzbek and present them in the Uzbek news categorization task.

KW - CNN

KW - Uzbek

KW - multi-class classification

KW - news categorization

KW - word embeddings

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U2 - 10.1109/AICT50176.2020.9368822

DO - 10.1109/AICT50176.2020.9368822

M3 - Conference contribution

SN - 9781728173870

T3 - 14th IEEE International Conference on Application of Information and Communication Technologies, AICT 2020 - Proceedings

BT - 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Proceedings

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - The 14th IEEE International Conference on Application of Information and Communication Technologies

Y2 - 7 October 2020 through 9 October 2020

ER -

Rabbimov I, Kobilov S, Mporas I. Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks. In 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT) Proceedings. Institute of Electrical and Electronics Engineers (IEEE). 2021. (14th IEEE International Conference on Application of Information and Communication Technologies, AICT 2020 - Proceedings). doi: 10.1109/AICT50176.2020.9368822

Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks

Abstract

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Keywords

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