TY - GEN
T1 - Extracting loosely structured data records through mining strict patterns
AU - Wu, Yipu
AU - Chen, Jing
AU - Li, Qing
PY - 2008/10/1
Y1 - 2008/10/1
N2 - Extracting loosely structured data records (DRs) has wide applications in many domains, such as forum pattern recognition, blog data analysis, and books and news review analysis. Currently existing methods work well for strongly structured DRs only. In this paper, we address the problem of extracting loosely structured DRs through mining strict patterns. In our method, we utilize both content feature and tag tree feature to recognize the loosely structured DRs, and propose a new approach to extract the DRs automatically. Through experimental study we demonstrate that this method is both effective and robust in practice.
AB - Extracting loosely structured data records (DRs) has wide applications in many domains, such as forum pattern recognition, blog data analysis, and books and news review analysis. Currently existing methods work well for strongly structured DRs only. In this paper, we address the problem of extracting loosely structured DRs through mining strict patterns. In our method, we utilize both content feature and tag tree feature to recognize the loosely structured DRs, and propose a new approach to extract the DRs automatically. Through experimental study we demonstrate that this method is both effective and robust in practice.
UR - http://www.scopus.com/inward/record.url?scp=52649084677&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2008.4497543
DO - 10.1109/ICDE.2008.4497543
M3 - Conference article published in proceeding or book
AN - SCOPUS:52649084677
SN - 9781424418374
T3 - Proceedings - International Conference on Data Engineering
SP - 1322
EP - 1324
BT - Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08
T2 - 2008 IEEE 24th International Conference on Data Engineering, ICDE'08
Y2 - 7 April 2008 through 12 April 2008
ER -