creators_name: Wang, Junfeng creators_name: He, Xiaofei creators_name: Wang, Can creators_name: Pei, Jian creators_name: Bu, Jiajun creators_name: Chen, Chun creators_name: Guan, Ziyu creators_name: Gang, Lu type: conference_item datestamp: 2009-04-06 19:12:23 lastmod: 2009-04-07 14:02:50 metadata_visibility: show title: News Article Extraction with Template-Independent Wrapper ispublished: pub full_text_status: public pres_type: poster abstract: We consider the problem of template-independent news extraction. The state-of-the-art news extraction method is based on template-level wrapper induction, which has two serious limitations. 1) It cannot correctly extract pages belonging to an unseen template until the wrapper for that template has been generated. 2) It is costly to maintain up-to-date wrappers for hundreds of websites, because any change of a template may lead to the invalidation of the corresponding wrapper. In this paper we formalize news extraction as a machine learning problem and learn a template-independent wrapper using a very small number of labeled news pages from a single site. Novel features dedicated to news titles and bodies are developed respectively. Correlations between the news title and the news body are exploited. Our template-independent wrapper can extract news pages from different sites regardless of templates. In experiments, a wrapper is learned from 40 pages from a single news site. It achieved 98.1% accuracy over 3,973 news pages from 12 news sites. date: 2009-04 pagerange: 1085-1085 event_title: 18th International World Wide Web Conference event_location: Madrid, Spain event_dates: April 20th-24th, 2009 event_type: conference refereed: TRUE citation: Wang, Junfeng and He, Xiaofei and Wang, Can and Pei, Jian and Bu, Jiajun and Chen, Chun and Guan, Ziyu and Gang, Lu (2009) News Article Extraction with Template-Independent Wrapper. In: 18th International World Wide Web Conference, April 20th-24th, 2009, Madrid, Spain. document_url: http://www2009.eprints.org/123/1/p1085.pdf