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Hidden Markov Model-based Extraction of Internet Information  

Park, Dong-Chul (Dept. of Information Eng., Myong Ji University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.46, no.3, 2009 , pp. 8-14 More about this Journal
Abstract
A Hidden Markov Model(HMM)-based information extraction method is proposed in this paper. The proposed extraction method is applied to extraction of products' prices. The input of the proposed IESHMM is the URLs of a search engine's interface, which contains the names of the product types. The output of the system is the list of extracted slots of each product: name, price, image, and URL. With the observation data set Maximum Likelihood algorithm and Baum-Welch algorithm are used for the training of HMM and The Viterbi algorithm is then applied to find the state sequence of the maximal probability that matches the observation block sequence. When applied to practical problems, the proposed HMM-based system shows improved results over a conventional method, PEWEB, in terms of recall ration and accuracy.
Keywords
HMM; internet; data extraction; Baum-Welch algorithm; Viterbi algorithm;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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