如果其他用户选择了同样的个性化分类和关键字，并且属于同一个组，那么将访问相同的缓存条目。
Other users who have selected the same personalization categories and keywords, and who belong to the same group, will access the same cached items.
该示例数据展示了如何使用一个分类视图(VFCustomersKeywords)来获取关键字列表，即从该视图中的每个文档读出keywords字段。
The sample database shows how to use a categorized view (VFCustomersKeywords) to get a keyword list by reading the Keywords field out of each document in the view.
您还可以将构造型看作是这个单元的额外关键字对一个单元进一步进行分类，如清单6所示。
You can further classify a unit using stereotypes as an additional keyword on the unit, as shown in Listing 6.
目前已有的检索系统主要采用基于关键字的全文检索以及分类检索技术。
So far, retrieval system is mainly based on the keyword search and classification technology.
BLIMS系统包括信息自动分类与关键字提取子系统、信息自动文摘子系统和双语信息库BLIB及其存储与检索子系统。
There are three parts of BLIMS, i. e. auto-classifying and indexing system, auto-abstracting system, and BLIB storing and retrieving system.
智能搜索引擎优化链接可以自动链接您的文章，并与相应的职位，页面，您的职位分类和标签上的评论关键字和词组。
SEO Smart Links can automatically link keywords and phrases in your posts and comments with corresponding posts, pages, categories and tags on your posts.
传统的过滤方法简单地把关键字匹配作为分类的依据，常导致漏判误判等问题。
The traditional strategy based on simple keywords matching often leads to low accuracy of filtering.
传统的图像索引方法从图像数据库中按照关键字或号码，和按分类描述来检索引图像。
In traditional method of image retrieval searches images according to keys, number and sort describe from image database.
输出多种定量指标体系，分类组合查询和关键字综合查询，可动态添加的评测和预警模型：Z记分模型等。
Output of a variety of quantitative indicators system, an integrated clustering queries and keyword queries can be dynamically added evaluation and early warning models:Z score model.
然后，介绍了传统的基于关键字的向量空间模型的文本分类的几个重要阶段，并着重介绍了其中的文本表示的相关技术和两种经典分类算法。
Then, this paper eliminates ambiguity of word meanings in text by WordNet. A representation of text based on concept is proposed later, and has been also applied to classification in SVM and KNN.
然后，介绍了传统的基于关键字的向量空间模型的文本分类的几个重要阶段，并着重介绍了其中的文本表示的相关技术和两种经典分类算法。
Then, this paper eliminates ambiguity of word meanings in text by WordNet. A representation of text based on concept is proposed later, and has been also applied to classification in SVM and KNN.
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