介绍一种局部上下文分析(LCA)剪枝概念树的方法。
This paper proposes a novel query expansion method using Local Context Analysis (LCA) based concept tree pruning.
该方法通过设计一种客户端的用户兴趣挖掘模型，同时将用户兴趣模型与局部上下文分析方法相结合，克服了局部上下文分析的缺陷。
By mining the user profile in client computer, then combining user profile and traditional LCA, the method could resolve the defect of LCA.
分析结果显示，最常用的知识资源是局部上下文。
The results reveal that the most frequently used knowledge source was the local co-text .
该算法将全局分析和局部分析结合起来从单文档中抽取用户选定查询的上下文信息。
The algorithm combines the global analysis with the local analysis to extract the context information of the user's marked query from single document.
该算法将全局分析和局部分析结合起来从单文档中抽取用户选定查询的上下文信息。
The algorithm combines the global analysis with the local analysis to extract the context information of the user's marked query from single document.
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