Automated News Categorization using Machine Learning  methods

dc.contributor.author	Suleymanov, U.
dc.contributor.author	Rustamov, S.
dc.date.accessioned	2022-05-12T05:40:04Z
dc.date.available	2022-05-12T05:40:04Z
dc.date.issued	2018
dc.identifier.uri	http://hdl.handle.net/20.500.12181/368
dc.description.abstract	Being one of the most linguistically rich languages, Azerbaijani has been researched less in the context of natural language processing area. The text corpus created from Azerbaijani news articles is designed to apply supervised machine learning approaches for the case of automatic news labeling. Chi-squared test and LASSO methods have been implemented for feature selection and pre-processing. The application of supervised machine learning approaches to the text corpus allowed us to compare the performance results of well-established supervised machine learning approaches in the domain of Azerbaijani language.	en_US
dc.language.iso	en	en_US
dc.publisher	IOP Publishing	en_US
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject.lcsh	Machine learning.	en
dc.subject.lcsh	Artificial intelligence.	en
dc.subject.lcsh	Data processing.	en
dc.title	Automated News Categorization using Machine Learning methods	en_US
dc.type	Article	en_US