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Research on Text Retrieval Algorithm Based on Latent Semantic Analysis

Author: ZhaoYaHui
Tutor: CuiRongYi
School: Yanbian University
Course: Applied Computer Technology
Keywords: Text Information Retrieval Vector space model Latent Semantic Indexing Genetic Algorithms
CLC: TP391.3
Type: Master's thesis
Year: 2009
Downloads: 94
Quote: 2
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Abstract


Text information retrieval technology research goal is to identify and obtain the required text information from a large number of text messages collection. The popularity of the Internet in today's society, the text information retrieval technology has become an important way for the effective use of information resources, fast, fully absorbed and access to text information. This technology more and more urgent need for people's learning and scientific research is of great significance. Dissertation Research in centralized, efficient, high-quality text retrieval positioning semantically similar to the query text paragraphs of text retrieval strategies and algorithms. The text of this paper indicates that the basic model is the vector space model (SVM), the semantics means of expression is based on the latent semantic indexing (LSI) model, the search algorithm is based on a genetic algorithm (GA). The main work of this paper are as follows: (1) analysis of the structure of the latent semantic space. Processing lexical items - text matrix, and this matrix based on the singular value distribution characteristics of best approximation in the least square error sense using singular value decomposition method, latent semantic space projection matrix is ??constructed. Any text vectors can be expressed by the projection matrix of the latent semantic space, on the one hand, can effectively eliminate the correlation between the lexical items and the other hand, can suppress noise interference. (2) between the proposed text of the query text and large-capacity non-effective method of determining correlation. The query text vector the potential semantic space component and zero semantic space component, while latent semantic space component is less than a given threshold, to determine all the paragraphs in the text of the query text and large capacity are not similar, in search strategy can to abandon further details match. (3) design using genetic algorithms passage retrieval algorithm. Large enough the potential semantic space component of the query text, all the paragraphs in the space (a document) as a matching object, cosine similarity matching latent semantic space component of the query text. As a result of the genetic algorithm efficiently locate the approximate optimal paragraph; retrieval latent semantic space, positioning paragraphs semantically similar to the query text. Experimental results show that proposed in this paper based on latent semantic text search strategy and genetic algorithm-based text retrieval methods compared with traditional algorithms, has greatly improved the retrieval accuracy, recall and F-indicators but also in terms of retrieval efficiency of the proposed algorithm is also superior to traditional methods of text information retrieval. Therefore, the proposed strategy based on latent semantic text retrieval and text retrieval method based on genetic algorithms can be used for large-capacity text retrieval.

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