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Mining tables from large scale HTML texts
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Proceedings of the 18th conference on Computational linguistics - Volume 1 table of contents
Saarbrücken, Germany
Pages: 166 - 172  
Year of Publication: 2000
ISBN:1-55860-717-X
Authors
Hsin-Hsi Chen  National Taiwan University, Taipei, Taiwan, R.O.C.
Shih-Chung Tsai  National Taiwan University, Taipei, Taiwan, R.O.C.
Jin-He Tsai  National Taiwan University, Taipei, TAIWAN, R.O.C.
Sponsors
: Deutsches Forschungszentrum fiir Ktinstliche Intelligenz (DFKI)
: Loria
: Ministète de la Recherche Français
: Centre Universitaire de Luxembourg
: Deutsche Forschungsgemeinschaft
: Ministerium für Bildung, Kultur und Wissenschaft des Saarlandes
: Université; Nancy 2
: Universität des Saarlandes
Publisher
Association for Computational Linguistics  Morristown, NJ, USA
Bibliometrics
Downloads (6 Weeks): n/a,   Downloads (12 Months): n/a,   Citation Count: 17
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DOI Bookmark: 10.3115/990820.990845

ABSTRACT

Table is a very common presentation scheme, but few papers touch on table extraction in text data mining. This paper focuses on mining tables from large-scale HTML texts. Table filtering, recognition, interpretation, and presentation are discussed. Heuristic rules and cell similarities are employed to identify tables. The F-measure of table recognition is 86.50%. We also propose an algorithm to capture attribute-value relationships among table cells. Finally, more structured data is extracted and presented.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
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2
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3
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4
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9
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12

CITED BY  17
 
 
 
 
 
 
Collaborative Colleagues:
Hsin-Hsi Chen: colleagues
Shih-Chung Tsai: colleagues
Jin-He Tsai: colleagues

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