This item is a Poster.
- Wang, Junfeng - Zhejiang University
- He, Xiaofei - Zhejiang University
- Wang, Can - Zhejiang University
- Pei, Jian - Simon Fraser University
- Bu, Jiajun - Zhejiang University
- Chen, Chun - Zhejiang University
- Guan, Ziyu - Zhejiang University
- Gang, Lu - Zhejiang University of Finance and Economics
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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.
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