Topic Analysis of Chinese Text Based on Small World Model

The paper aims to perform topic spotting of segments based on text segmentation using small world structure.Main topic of the whole text is generalized and the skeleton of text shows itself.It is explained that the term co-occurrence graph of text is highly clustered and has short path length,which proves that texts have small world structure.Clusters in the small world structure are detected.The density of each cluster is computed to discover the boundary of a segment.Topic words are extracted from clusters of the graph.Words which are not distinctly in the analyzed text can be included to express the topics with the help of word clustering of background and topic words association.The signification behind the words are attempted to dig out.Although much research on applications of small world structure,it is a new task to analyze texts with the characteristics of small world.The experiments tell that the result of tests is far better than that of other methods.Valuable pre-processing is provided for next work of text reasoning.