Learning to Extract Hierarchical Information from Semi-structured Documents

International Conference on Information and Knowledge Management, Proceedings

Volumn 2000-January, Issue , 2000, Pages 250-257

  Lin, Wai Yip ^a   Lam, Wai ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

EXTRACTION; LEARNING SYSTEMS;

DOCUMENT STRUCTURE; EXTRACTION RULE; HIERARCHICAL INFORMATION; LEARNING METHODS; LEARNING TASKS; RULE LEARNING; SEMI-STRUCTURED DOCUMENTS; SIMPLE STRUCTURES; STRUCTURE EXTRACTION; STRUCTURE-LEARNING;

SEMANTICS;

EID: 85139646077     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/354756.354826     Document Type: Conference Paper

References (7)

1. N. Ashish and C. Knoblock. Wrapper generation for semi-structured Internet sources. SIGMOD Record, 26(4):8-15, Decemberber 1997.
2. M. Califf and R. Mooney. Relational learning of pattern-match rules for information extraction. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 328-334, July 1999.
3. S. Chawathe, H. Garcia-Molina, J. Hammer, K. Ireland, Y. Papakonstantinou, J. Ullman, and J. Widom. The TSIMMIS project: integration of heterogeneous information sources. In Proceedings of the Information Processing Society of Japan, pages 7-18, 1994.
4. W. Cohen. Recognizing structure in Web pages using similarity queries. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pages 59-66, July 1999.
5. D. W. Embley, D. Campbell, R. Smith, and S. Liddle. A conceptual-modeling approach to extracting data from the Web. In Proceedings of the 17th International Conference on Conceptual Modeling (ER'98), pages 78-91, November 1998.
6. D. W. Embley, Y. S. Jiang, and Y. K. Ng. Record-boundary discovery in Web documents. In SIGMOD 1999, Proceedings ACM SIGMOD International Conference on Management of Data, pages 467-478, June 1999.
7. D. Freitag. Information extraction from HTML: Application of a general machine learning approach. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 517-523, July 1998.