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Chinese text automatic classification system research - the design of the Chinese word segmentation and classification

Author: YangXiaoGuang
Tutor: WangZhongRen
School: University of Electronic Science and Technology
Course: Computer System Architecture
Keywords: Text Classification Automatic segmentation Classifier Support Vector Machine
CLC: TP391.1
Type: Master's thesis
Year: 2004
Downloads: 474
Quote: 3
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Abstract


With the rapid development of network information, information processing has become a tool for people to obtain useful information indispensable. Automatic text classification systems is an important research direction of information processing. It is in the given classification system, based on the contents of the text automatically determine the text category, has a very important practical significance, e-government, online publishing, network information retrieval services, public electronic library and large-scale real Corpus construction and other fields has broad application prospects. From the application-oriented, facing large-scale, real text and actual demand-oriented perspective, the study of Chinese text automatic classification system from the following aspects: First, the Chinese automatic segmentation techniques, including crude words segmentation and unknown word recognition, part of speech tagging disambiguation. Words rough segmentation, we integrated the shortest path method with full segmentation method proposed a rough segmentation of Chinese words rough segmentation model based on N-shortest path statistics; unknown word recognition, were numeral phrases, overlapping words and name recognition of different identification methods. Which, in the name identification process, we use the Viterbi algorithm to determine the sentence, the maximum probability of context information state sequence and combination of the local statistics text matching to identify the names, places, translation; For the part of speech labeling disambiguation, we are using the CLAWS algorithm of thinking, the various speech tags in conjunction with each word has a different probability characteristics, based on a hidden Markov model. Followed classifier design, we focus on solving the feature words extracted text representation using support vector machine classifier design implementation. Extraction of feature words based on Shannon information theories removed from each type of text set in the high-frequency word thesaurus disable word thesaurus words, each category corresponding to the type of word thesaurus, design feature words weighting function based on the extraction and weighted feature words; text represents a problem, we have adopted is based on the digital representation of the vector space model method, using text features units form a vector space, the text was finally formalized as N dimensional space vector D; classifier design implementation, according to the characteristics of the various categories of text data, proposed a linear separable support vector machine based on information, according to the number of samples of the training learning rejection , added to its optimal classification surface a relaxation of η improved method to achieve a text classification method based on support vector machine, and achieved a satisfactory classification.

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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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