Abstract: Topic discovery from Web page has an important impact on natural language processing,such as text classification,automatic abstract generation,information fusion etc.Mining Web page topics can help users better understand the content of Web pages.Although there are some papers discussing topic discovery from ordinary texts,few of them consider how the label a word belongs to and the location in which a word appears affect the weight of a word,and none of them gives calculation methods for the two impact factors.This article extended Web topics from words level to concepts level based on WordNet,used speech tagging to determine the POS of the words,used word sense disambiguation to determine the words’ meaning in the pages,made full use of label impact factor and location impact factor to modify the weights of concepts,and proposed calculation formulas for calculating these two impact factors.Experimental results on DMOZ dataset show that,compared with un-adjusted weight method,the adjusted weights method can significantly improve topic mining accuracy,which can reach up to 0.95 in the best case.

Key words: Speech tagging,Word sense disambiguation,Label impact factor,Location impact factor,Adjusted weights