Thumbnail
Access Restriction
Subscribed

Author Lai, Jianbing ♦ Liu, Qiang ♦ Liu, Yi
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Data mining ♦ Hidden Markov models ♦ Voting ♦ Dictionaries ♦ Collaborative work ♦ Probability distribution ♦ State estimation ♦ Viterbi algorithm ♦ Algorithm design and analysis ♦ Internet ♦ voting strategy ♦ hidden Markov model ♦ semantic block ♦ semi-structure
Abstract This paper proposes a semantic-block-based hidden Markov model. Semantic block is segmented from the elicited information of various websites based on their characteristic of semi-structure. The model adopts semantic block as the basic element in an observation sequence, replacing the original element — word, in order to improve the accuracy and efficiency of the transition matrix. Also, it optimizes the observation probability distribution and the estimation accuracy of state transition sequence by adopting the “voting strategy” and modifying Viterbi algorithm. In the end, the experiment results are able to show that the new model and algorithms give satisfying performance in recall and precision for web information extraction.
Description Author affiliation: School of Software, Tsinghua University, Beijing, China (Lai, Jianbing; Liu, Qiang; Liu, Yi)
ISBN 9781424467631
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-04-14
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424467631
Size (in Bytes) 196.72 kB
Page Count 5
Starting Page 234
Ending Page 238


Source: IEEE Xplore Digital Library