Using text mining techniques for extracting information from research articles

Nowaday s, research in text mining has become one of the widespread fields in analyzing natural language documents. The present study demonstrates a comprehensive overview about text mining and its current research status. As indicated in the literature, there is a limitation in addressing Informati...

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Main Authors:	Salloum, Said A., Al-Emran, Mostafa, Monem, A. A., Shaalan, Khaled
Format:	Book Section
Language:	English
Published:	Springer 2017
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/21622/ http://umpir.ump.edu.my/id/eprint/21622/ http://umpir.ump.edu.my/id/eprint/21622/ http://umpir.ump.edu.my/id/eprint/21622/1/4.%20Using%20Text%20Mining%20Techniques%20for%20Extracting%20Information%20from%20Research%20Articles.pdf

id	ump-21622
recordtype	eprints
spelling	ump-216222019-03-20T04:19:10Z http://umpir.ump.edu.my/id/eprint/21622/ Using text mining techniques for extracting information from research articles Salloum, Said A. Al-Emran, Mostafa Monem, A. A. Shaalan, Khaled QA76 Computer software Nowaday s, research in text mining has become one of the widespread fields in analyzing natural language documents. The present study demonstrates a comprehensive overview about text mining and its current research status. As indicated in the literature, there is a limitation in addressing Information Extraction from research articles using Data Mining techniques. The synergy between them helps to discover different interesting text patterns in the retrieved articles. In our study, we collected, and textually analyzed through various text mining techniques, three hundred refereed journal articles in the field of mobile learning from six scientific databases, namely: Springer, Wiley, Science Direct, SAGE, IEEE, and Cambridge. The selection of the collected articles was based on the criteria that all these articles should incorporate mobile learning as the main component in the higher educational context. Experimental results indicated that Springer database represents the main source for research articles in the field of mobile education for the medical domain. Moreover, results where the similarity among topics could not be detected were due to either their interrelations or ambiguity in their meaning. Furthermore, findings showed that there was a booming increase in the number of published articles during the years 2015 through 2016. In addition, other implications and future perspectives are presented in the study. © 2018, Springer International Publishing AG. Springer 2017-11-18 Book Section PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/21622/1/4.%20Using%20Text%20Mining%20Techniques%20for%20Extracting%20Information%20from%20Research%20Articles.pdf Salloum, Said A. and Al-Emran, Mostafa and Monem, A. A. and Shaalan, Khaled (2017) Using text mining techniques for extracting information from research articles. In: Intelligent Natural Language Processing: Trends and Applications. Springer, Berlin, Germany, pp. 373-397. ISBN 9783319670553 https://doi.org/10.1007/978-3-319-67056-0_18 https://doi.org/10.1007/978-3-319-67056-0_18
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Salloum, Said A. Al-Emran, Mostafa Monem, A. A. Shaalan, Khaled Using text mining techniques for extracting information from research articles
description	Nowaday s, research in text mining has become one of the widespread fields in analyzing natural language documents. The present study demonstrates a comprehensive overview about text mining and its current research status. As indicated in the literature, there is a limitation in addressing Information Extraction from research articles using Data Mining techniques. The synergy between them helps to discover different interesting text patterns in the retrieved articles. In our study, we collected, and textually analyzed through various text mining techniques, three hundred refereed journal articles in the field of mobile learning from six scientific databases, namely: Springer, Wiley, Science Direct, SAGE, IEEE, and Cambridge. The selection of the collected articles was based on the criteria that all these articles should incorporate mobile learning as the main component in the higher educational context. Experimental results indicated that Springer database represents the main source for research articles in the field of mobile education for the medical domain. Moreover, results where the similarity among topics could not be detected were due to either their interrelations or ambiguity in their meaning. Furthermore, findings showed that there was a booming increase in the number of published articles during the years 2015 through 2016. In addition, other implications and future perspectives are presented in the study. © 2018, Springer International Publishing AG.
format	Book Section
author	Salloum, Said A. Al-Emran, Mostafa Monem, A. A. Shaalan, Khaled
author_facet	Salloum, Said A. Al-Emran, Mostafa Monem, A. A. Shaalan, Khaled
author_sort	Salloum, Said A.
title	Using text mining techniques for extracting information from research articles
title_short	Using text mining techniques for extracting information from research articles
title_full	Using text mining techniques for extracting information from research articles
title_fullStr	Using text mining techniques for extracting information from research articles
title_full_unstemmed	Using text mining techniques for extracting information from research articles
title_sort	using text mining techniques for extracting information from research articles
publisher	Springer
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/21622/ http://umpir.ump.edu.my/id/eprint/21622/ http://umpir.ump.edu.my/id/eprint/21622/ http://umpir.ump.edu.my/id/eprint/21622/1/4.%20Using%20Text%20Mining%20Techniques%20for%20Extracting%20Information%20from%20Research%20Articles.pdf
first_indexed	2023-09-18T22:31:49Z
last_indexed	2023-09-18T22:31:49Z
_version_	1777416335815344128

Using text mining techniques for extracting information from research articles

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