A Latent Semantic Analysis Based Method of Getting the Category Attribute of Words

机译：基于潜在语义分析的单词类别属性方法

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摘要

Current search engines have two problems, losing useful information and including useless information. These two problems are aroused by the keyword matching retrieval model, which is adopted by almost all search engines. We introduce the conception of category attribute of a word. According to the category attribute of a word, the useless results can be removed from the search results and the retrieval efficiency will be improved. A latent semantic analysis based method of getting the category attribute of the word is presented in this paper, which is proved to be effective by experiment. Latent semantic analysis is a method that can discover the underlying semantic relation between words and documents. Singular value decomposition is used in latent semantic analysis to analyze the words and documents and get the semantic relation finally.

机译：当前搜索引擎有两个问题，丢失有用信息并包括无用的信息。这两个问题是由关键字匹配的检索模型引起的，几乎所有搜索引擎都采用。我们介绍了单词的类别属性的概念。根据单词的类别属性，可以从搜索结果中删除无用的结果，并将提高检索效率。本文介绍了基于获取单词类别属性的基于潜在语义分析的方法，被证明是通过实验有效的。潜在语义分析是一种方法，可以发现单词和文档之间的底层语义关系。奇异值分解用于潜在语义分析，分析单词和文档并最终获得语义关系。

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《International Conference on Electronic Computer Technology》|2009年||共6页
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作者
Zongli Jiang; Changdong Lu;
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中图分类 TP3-53;
关键词
Information retrieval; Search engine; Latent semantic analysis; Text categorization;

机译：信息检索;搜索引擎;潜在语义分析;文本分类;

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A Latent Semantic Analysis Based Method of Getting the Category Attribute of Words

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