DocumentCode
1585036
Title
Web sites thematic classification using hidden Markov models
Author
Serradura, Lyonel ; Slimane, Mohamed ; Vincent, Nicole
Author_Institution
Lab. d´´Inf., Tours Univ., France
fYear
2001
fDate
6/23/1905 12:00:00 AM
Firstpage
1094
Lastpage
1098
Abstract
There is more and more information available on the Internet. We need tools to help us extract the right piece of information. We have developed a classification algorithm tackling this issue in French. It distinguishes web pages classifying their text content into themes. We use Hidden Markov Models (HMM) to build this method named STCoL (Supervised Thematic Corpus Learning). Once themes are modeled with HMMs, STCoL is able to classify documents from different sources. This method is not only efficient but is also robust
Keywords
Internet; classification; hidden Markov models; information resources; French; Hidden Markov Models; Internet; STCoL; Supervised Thematic Corpus Learning; classification algorithm; text content; thematic classification; web pages; Data mining; Hidden Markov models; Internet; Law; Legal factors; Portals; Robustness; Waste materials; Web pages;
fLanguage
English
Publisher
ieee
Conference_Titel
Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on
Conference_Location
Seattle, WA
Print_ISBN
0-7695-1263-1
Type
conf
DOI
10.1109/ICDAR.2001.953955
Filename
953955
Link To Document