Topic Detection and Tracking for Chinese News Web Pages

With the continuous growth in the number of available Web news sites and the diversity in their presentation of content, there is an increasing need in mining the news correlation on the Web to keep tracking of successive development of specific event. In this paper a new approach of topic tracking of Chinese news Web pages is presented. Temporal information extracted from news texts and "key Web contexts" extracted from HTML documents is used to improve the performance of dependency structure language model (DSLM). Experimental results are examined that shows the usefulness of our approach.