Mining tables from large scale html texts

Mining tables from large scale html texts (2000)

by Hsin-hsi Chen , Shih-chung Tsai , Jin-he Tsai

Venue:	In Proceedings of the 18th International Conference on Computational Linguistics (COLING’00
Citations:	31 - 0 self

Datum	Value	Source
TITLE	Mining tables from large scale html texts	INFERENCE
AUTHOR NAME	Hsin-hsi Chen	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science and Information Engineering; National Taiwan University	SVM HeaderParse 0.2
AUTHOR ADDR	Taipei, TAIWAN, R.O.C.	SVM HeaderParse 0.2
AUTHOR NAME	Shih-chung Tsai	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science and Information Engineering; National Taiwan University	SVM HeaderParse 0.2
AUTHOR ADDR	Taipei, TAIWAN, R.O.C.	SVM HeaderParse 0.2
AUTHOR NAME	Jin-he Tsai	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science and Information Engineering; National Taiwan University	SVM HeaderParse 0.2
AUTHOR ADDR	Taipei, TAIWAN, R.O.C.	SVM HeaderParse 0.2
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.	SVM HeaderParse 0.2
YEAR	2000	INFERENCE
VENUE	In Proceedings of the 18th International Conference on Computational Linguistics (COLING’00	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	166--172	INFERENCE
CITATIONS	11 found	ParsCit 1.0