Authors:
Nina Khairova
1
;
Anastasiia Kolesnyk
1
;
Orken Mamyrbayev
2
and
Kuralay Mukhsina
3
Affiliations:
1
National Technical University ”Kharkiv Polytechnic Institute”, 2, Kyrpychova str., 61002, Kharkiv and Ukraine
;
2
Institute of Information and Computational Technologies, 125, Pushkin str., 050010, Almaty and Republic of Kazakhstan
;
3
Al-Farabi Kazakh National University, 71 al-Farabi Ave., Almaty and Republic of Kazakhstan
Keyword(s):
Text Quality, Readability Indexes, Linguistic Features, Statistical Characteristics of a Document, Simple Wikipedia, Enterprise Information Systems.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Enterprise Information Systems
;
HCI on Enterprise Information Systems
;
Human-Computer Interaction
;
Natural Language Interfaces to Intelligent Systems
;
Society, e-Business and e-Government
;
Software Agents and Internet Computing
;
Web 2.0 and Social Networking Controls
;
Web Information Systems and Technologies
Abstract:
Currently, businesses increasingly use various external big data sources for extracting and integrating information into their own enterprise information systems to make correct economic decisions, to understand customer needs, and to predict risks. The necessary condition for obtaining useful knowledge from big data is analysing high-quality data and using quality textual data. In the study, we focus on the influence of readability and some particular features of the texts written for a global audience on the texts quality assessment. In order to estimate the influence of different linguistic and statistical factors on the text readability, we reviewed five different text corpora. Two of them contain texts from Wikipedia, the third one contains texts from Simple Wikipedia and two last corpora include scientific and educational texts. We show linguistic and statistical features of a text that have the greatest influence on the text quality for business corporations. Finally, we propo
se some directions on the way to automatic predicting the readability of texts in the Web.
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