A Signal-Representation-Based Parser to Extract Text-Based Information from the Web
Mu-Chun Su*1, Shao-Jui Wang*2, Chen-Ko Huang*3,
Pa-ChunWang*4, *5, Fu-Hau Hsu*1, Shih-Chieh Lin*1,
and Yi-Zeng Hsieh*1
*1Department of Computer Science & Information Engineering, National Central University, Taiwan
*2Chunghwa Telecom Co., Ltd., Taiwan
*3COMPAL ELECTRONIC, INC, Taiwan
*4Quality Management Center, Cathay General Hospital, Taiwan, R.O.C.
*5School of Medicine, Fu Jen Catholic University, Taiwan
Most of the dramatically increased amount of information available on the World Wide Web is provided via HTML and formatted for human browsing rather than for software programs. This situation calls for a tool that automatically extracts information from semistructured Web information sources, increasing the usefulness of value-added Web services. We present a signal-representation-based parser (SIRAP) that breaks Web pages up into logically coherent groups – groups of information related to an entity, for example. Templates for records with different tag structures are generated incrementally by a Histogram-Based Correlation Coefficient (HBCC) algorithm, then records on a Web page are detected efficiently using templates generated by matching. Hundreds of Web pages from 17 state-of-the-art search engines were used to demonstrate the feasibility of our approach.
Pa-ChunWang, Fu-Hau Hsu, Shih-Chieh Lin, and
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