NDLI: A novel post-classifier for search engine using hidden Markov model

Content Provider	IEEE Xplore Digital Library
Author	Zhi-Wu Liao Lam, E.C.M. Tang, Y.Y.
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci., Hong Kong Baptist Univ., China (Zhi-Wu Liao; Lam, E.C.M.; Tang, Y.Y.)
Abstract	Hidden Markov models (HHMs), while well applied in fields such as speech recognition and optical character recognition, have not been used in post-classification for search engines. We explore the use of HMMs for optimization of search engines tasks, specifically focusing on how to construct a new model structure to improve the classification of web pages. We show that a manually constructed new structure model that contains only two states and two classes of observations per field can produce good classification results, and discuss strategies for learning the model structure automatically from data. We also demonstrate that the use of new structure model to classify the search results using some search engines and some different search keywords provide a significant improvement in search accuracy. Our models are applied to the task of post-classifying the web pages selected by the search engine Google, and achieve a classification accuracy of 93.4.
Sponsorship	Hebei Univ. IEEE Syst., Man & Cybernetics Tech. Committe on Cybernetics
Starting Page	1926
Ending Page	1928
File Size	217140
Page Count	3
File Format	PDF
ISBN	0780375084
DOI	10.1109/ICMLC.2002.1175373
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Search engines Hidden Markov models Web pages Data mining World Wide Web Computer science Electronic mail Speech recognition Web sites HTML
Content Type	Text
Resource Type	Article

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