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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 723469, 9 pages
Research Article

Text Matching and Categorization: Mining Implicit Semantic Knowledge from Tree-Shape Structures

Lin Guo,1,2 Wanli Zuo,1,2 Tao Peng,1,2 and Lin Yue1,2

1College of Computer Science and Technology, Jilin University, Jilin 130000, China
2Symbol Computation and Knowledge Engineer of Ministry of Education, Jilin University, Jilin 130000, China

Received 31 March 2015; Accepted 9 June 2015

Academic Editor: Chaudry Masood Khalique

Copyright © 2015 Lin Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the proposed algorithm remarkably reduces the time and effort spent in training and classifying, which outperforms established competitors in correctness and effectiveness.