Journal of Applied Science and Engineering

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Turdi Tohti1 , Xing Tan2 , Jimmy Huang2 , and Askar Hamdulla This email address is being protected from spambots. You need JavaScript enabled to view it.1

1School of Information Science and Engineering, Xinjiang University, China
2Information Retrieval & Knowledge Management Research Lab, York University, Canada


Received: June 14, 2020
Accepted: December 5, 2020
Publication Date: June 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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In Uyghur language, the words which are segmented by inter-word space as natural separator can hardly serve as features in text representation, which leads to the low efficiency of text processing, it is still a research topic how to use language units beyond word boundaries as features to represent texts and improve the efficiency of text processing. This paper proposes a semantic string extraction approach, which is a method for extracting language units beyond word boundaries. At the same time, it also proposes the methods for textual representation and similarity measurement, and verifies its effectiveness in Uyghur text clustering tasks. Specifically, a combination of string expansion and language rules are applied to identify the trusted frequent patterns (TFP) in the text set. Next, semantic strings are evaluated and selected from the text set. Regarding similarity measure, each text is represented as a weighted semantic string set, and a set-based text similarity measuring approach is presented. Finally, the above ideas and approaches are applied to the Uyghur text clustering, and the corresponding clustering algorithms are proposed and verified through series of experiments on the large-scale text corpus. Experimental results show that the semantic string-based text representation is in general very useful in processing Uyghur language.

Keywords: Uyghur language; Frequent pattern discovery; Semantic string extraction; Text representation; Text clustering


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