Text classification based on sentence correlation
Youhua Zhang, Xiong Fanlun · 2006
A text category model based on sentence correlation(TCSC) was presented,which incrementally updates category corpus with the training documents automatically.Then,category correlation was obtained by means of sentence position weight and corpus item weight to achieve correlation matrix for text classification.This model avoids the problem of word segmentation in Chinese documents and lowers the effect of words with multiple meanings in the phase of classification.Experimental results show that the recall and precision of this model reached of over 86%,and can be improved by updating corpus.This model can also be implemented easily in programming.