Topic Detection and Tracking for Chinese News Web Pages

Author

Qiu, Jing ; Liao, Lejian ; Dong, Xiujie

Author_Institution

Sch. of Comput. Sci., Beijing Inst. of Technol., Beijing

fYear

2008

fDate

23-25 July 2008

Firstpage

114

Lastpage

120

Abstract

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.

Keywords

Web sites; information retrieval; Chinese news Web pages; HTML documents; Web news correlation mining; Web news sites; dependency structure language model; key Web contexts; news texts; temporal information extraction; topic detection; Computer science; Context modeling; Data mining; Electronic mail; Event detection; HTML; Information technology; Laboratories; Natural languages; Web pages; Topic tracking; content extraction; dependency structure language model; temporal information extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Language Processing and Web Information Technology, 2008. ALPIT '08. International Conference on

Conference_Location

Dalian Liaoning

Print_ISBN

978-0-7695-3273-8

Type

conf

DOI

10.1109/ALPIT.2008.31

Filename

4584352