Abstract:

A topic link extraction method from Hub page based on extended label tree was proposed. Firs, a topic link sorted list was build and deny rules were learned by prefix tree, then, the link type was pre-determined. Second, by group splitting and re-merging, each candidate link was classified into different groups. The group type and the group which represented the hub page’s core region were identified, and finally all links were put into three different collections. Experimental results show that this method can achieve high-precision for topic link extraction without training.

Key words: link extraction; extended label tree; link prefix tree