Topic Feature Extraction of Chinese News Title

Author

Sun, Qiao ; Xu, Fu ; Zhibo, Chen

Author_Institution

Sch. of Comput. Sci. & Eng., Beihang Univ., Beijing, China

Volume

1

fYear

2011

fDate

24-25 Sept. 2011

Firstpage

367

Lastpage

370

Abstract

To attract first attention at a glance, news titles are often short and contain important abstract information of web news. Topic feature extraction of web news title can greatly help news processing system to improve efficiency and accuracy before process whole news text. After segmentation and tagging, some words are wrongly truncated into discontinuous characters and phrases are into separate words as well. This paper proposes a topic feature extracting model from Chinese web news titles on phrase granularity. Titles are truncated into tagged key words before using frequent patterns to combine words into phrases, which are topic features. We conduct experimental studies on corpus of Chinese news titles between March 2011 and June 2011. The result showed that our topic extraction approach can yield quite reasonable topic feature phrases.

Keywords

information resources; text analysis; Chinese Web news titles; Chinese news title; news processing system; phrase granularity; segmentation; tagging; topic feature extracting model; topic feature extraction; Conferences; Data mining; Educational institutions; Event detection; Feature extraction; Knowledge management; Tagging; extraction; frequent pattern; news title; topic feature;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology, Computer Engineering and Management Sciences (ICM), 2011 International Conference on

Conference_Location

Nanjing, Jiangsu

Print_ISBN

978-1-4577-1419-1

Type

conf

DOI

10.1109/ICM.2011.300

Filename

6113433