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Research on Web Text Categorization Technology Oriented to Information Service

Author: SuiFuNing
Tutor: YangQiang
School: National University of Defense Science and Technology
Course: Management Science and Engineering
Keywords: Information Services Feature selection User Modeling Text Classification Neural Networks
CLC: TP391.1
Type: Master's thesis
Year: 2010
Downloads: 56
Quote: 1
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Abstract


The development of information technology makes more complex and differentiated network user demand for information resources. From the mass of network information quickly and accurately find the user demand for information and data information services research. With the development of information networks at the same time, the scope of the information service gradually expanded service in the development towards a higher level of depth and content. The two main factors to affect the quality of information services to the clients' needs and describe the accuracy of the accuracy that the accuracy of the service object model and data mining. The interest and needs of the service object description inaccurate fundamentally determine the level of targeted and personalized information services; while the level of data mining also direct a significant impact on the quality of information services. This article is based on two major factors, the service object modeling and data mining areas of the Chinese classification techniques targeted research. Object modeling technique of information services, as well as Chinese text classification techniques are summarized, mainly to discuss the information filtering, user interest described based information services, as well as Chinese text segmentation, textual data representation of the text feature extraction, text classification Construction of main technical and other text classification process. Compare a rule-based segmentation methods and statistical sub-word method-based differences in feature extraction in information gain and CHI statistics, mutual information and other characteristics of select methods and potential semantic indexing (LSI) and other characteristics extraction methods, comparison of each of the superiority and inferiority; described in the text classification the Naive Bayes method, KNN method and support vector machine (SVM) classifier principle; discussed building rules as well as in English Corpus Corpus build status quo. In the analysis of traditional feature dimensionality reduction method based on information gain to improve a new feature selection algorithm to improve the algorithm by absolute concept and the way to eliminate interference characteristics, helping to eliminate information gain characteristics of interference in the process; characteristics of the foreign-based Knowledge Base extraction method attempts Chinese Knowledge Base, and analyzes their advantages and disadvantages; tried by reference to the Web link structure analysis, and PageRank algorithm, web-based structural information feature weight adjustment TermRank, the compared experimental and mature SVM classifier. Depth information service object description and modeling techniques and a complete description of the service object model, create, update method. Particularly in the Chinese classifier based on neural network classification methods discussed, minimum - maximum module network (Min-Max Modular) decomposition of combinatorial optimization method with the traditional BP neural network, using MATLAB simulation experiments.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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