A Generalized Hidden Markov Model Approach for Web Information Extraction

Author

Zhong, Ping ; Chen, Jinlin

Author_Institution

Dept. of Comput. Sci., City Univ. of New York, NY

fYear

2006

fDate

18-22 Dec. 2006

Firstpage

709

Lastpage

718

Abstract

A generalized hidden Markov model (GHMM) which extends traditional HMMs by making use of Web-specific information for Web information extraction is presented in this paper. Web content blocks are used instead of content terms as basic extraction unit in our approach. Besides, instead of using the traditional sequential state transition order, the state transition orders of GHMMs are detected based on layout structures of the corresponding Web pages. Furthermore, multiple emission features are applied instead of single emission feature. In this way GHMMs can better accommodate Web information extraction. Experiments show promising results of GHMMs

Keywords

Internet; hidden Markov models; information retrieval; Web content blocks; Web information extraction; generalized hidden Markov model approach; multiple emission features; state transition orders; Computer science; Data mining; Educational institutions; Hidden Markov models; Intelligent structures; Learning systems; Parameter estimation; State estimation; Stochastic processes; Web pages;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM International Conference on

Conference_Location

Hong Kong

Print_ISBN

0-7695-2747-7

Type

conf

DOI

10.1109/WI.2006.13

Filename

4061457