Using text mining techniques for extracting information from research articles

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

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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.

Item Type: Book Chapter
Uncontrolled Keywords: Text mining; Information extraction; Topic identification; Scientific databases; Mobile learning; Higher education
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 20 Mar 2019 04:19
Last Modified: 20 Mar 2019 04:19
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