Title :
Semantic conceptual primitives computing in text classification
Author :
Quan Zhang ; Yi Yuan ; Xiangfeng Wei ; Zhejie Chi ; Peimin Cong ; YiHua Du
Author_Institution :
Inst. of Acoust., Beijing, China
Abstract :
This paper presents a method for enhancing text classification performance with semantic computing. It adopts conceptual primitives with semantic relations as knowledge expression. Based on the semantic expression, it mined the association relation of primitives among different text classification, and these association rules take association relation as text classification feature. The presented method not only considers what kind of the semantic primitives that a text contains, but also takes account of the association relation of the semantic primitives. Moreover, we test the method with public text classification text set. The experiment result shows that, comparing with the commonly used methods, this method prompts text classification performance.
Keywords :
data mining; pattern classification; semantic networks; text analysis; association relation mining; association rules; knowledge expression; semantic conceptual primitives computing; semantic expression; semantic relations; text classification feature; text classification performance; Association rules; Computational modeling; Feature extraction; Semantics; Support vector machines; Text categorization; Training; association of concepts; association rule; primitives of concepts; semantic computing; text classification;
Conference_Titel :
Asian Language Processing (IALP), 2014 International Conference on
Conference_Location :
Kuching
DOI :
10.1109/IALP.2014.6973472