An Efficient Wikipedia Semantic Matching Approach to Text Document Classification

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An Efficient Wikipedia Semantic Matching Approach to Text Document Classification
Authors
Zongda Wu
Hui Zhu
Guiling Li
Zongmin Cui
Hui Huang
Jun Li
Enhong Chen
Guandong Xu
Publication date
2017
DOI
10.1016/j.ins.2017.02.009
Links
Original

An Efficient Wikipedia Semantic Matching Approach to Text Document Classification - scientific work related to Wikipedia quality published in 2017, written by Zongda Wu, Hui Zhu, Guiling Li, Zongmin Cui, Hui Huang, Jun Li, Enhong Chen and Guandong Xu.

Overview

A traditional classification approach based on keyword matching represents each text document as a set of keywords, without considering the semantic information, thereby, reducing the accuracy of classification. To solve this problem, a new classification approach based on Wikipedia matching was proposed, which represents each document as a concept vector in the Wikipedia semantic space so as to understand the text semantics, and has been demonstrated to improve the accuracy of classification. However, the immense Wikipedia semantic space greatly reduces the generation efficiency of a concept vector, resulting in a negative impact on the availability of the approach in an online environment. In this paper, authors propose an efficient Wikipedia semantic matching approach to document classification. First, authors define several heuristic selection rules to quickly pick out related concepts for a document from the Wikipedia semantic space, making it no longer necessary to match all the concepts in the semantic space, thus greatly improving the generation efficiency of the concept vector. Second, based on the semantic representation of each text document, authors compute the similarity between documents so as to accurately classify the documents. Finally, evaluation experiments demonstrate the effectiveness of approach, i.e., which can improve the classification efficiency of the Wikipedia matching under the precondition of not compromising the classification accuracy.

Embed

Wikipedia Quality

Wu, Zongda; Zhu, Hui; Li, Guiling; Cui, Zongmin; Huang, Hui; Li, Jun; Chen, Enhong; Xu, Guandong. (2017). "[[An Efficient Wikipedia Semantic Matching Approach to Text Document Classification]]". Elsevier. DOI: 10.1016/j.ins.2017.02.009.

English Wikipedia

{{cite journal |last1=Wu |first1=Zongda |last2=Zhu |first2=Hui |last3=Li |first3=Guiling |last4=Cui |first4=Zongmin |last5=Huang |first5=Hui |last6=Li |first6=Jun |last7=Chen |first7=Enhong |last8=Xu |first8=Guandong |title=An Efficient Wikipedia Semantic Matching Approach to Text Document Classification |date=2017 |doi=10.1016/j.ins.2017.02.009 |url=https://wikipediaquality.com/wiki/An_Efficient_Wikipedia_Semantic_Matching_Approach_to_Text_Document_Classification |journal=Elsevier}}

HTML

Wu, Zongda; Zhu, Hui; Li, Guiling; Cui, Zongmin; Huang, Hui; Li, Jun; Chen, Enhong; Xu, Guandong. (2017). &quot;<a href="https://wikipediaquality.com/wiki/An_Efficient_Wikipedia_Semantic_Matching_Approach_to_Text_Document_Classification">An Efficient Wikipedia Semantic Matching Approach to Text Document Classification</a>&quot;. Elsevier. DOI: 10.1016/j.ins.2017.02.009.