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 .
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.
本文提出了全局上下文属性和局部上下文属性两类属性作为分类模型的特征属性。
Two kinds of features, global context feature and local context feature, are proposed as the classification features.
文本语境简单的说就是文本的上下文，理解句子必须结合局部上下文和全局上下文两个方面。
Simply, textual context is just co-text, and the comprehension of sentences must depend on two aspects: local-context and whole-context.
对称窗口指上下文边界与歧义词的左右距离相等，大部分消歧系统凭经验将其作为最优的局部上下文窗口，很少选择非对称窗口。
Most systems choose optimal local context window on empirical grounds, which is usually symmetric, the same distance from the ambiguous word to both sides of the window.
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.
另一方面，局部声明如果是在不同的上下文中，则不会产生冲突。
Local declarations, on the other hand, cause no conflict if they have different contexts.
局部名对于其他上下文是不透明的，这使命名冲突的风险降到了最低。
The local names are opaque to other contexts, which minimizes the risks of name conflicts.
局部元素的上下文局限于它的当前位置，所以不能从模式的其他部分引用它。
A local element's context is limited to its current location, so it cannot be referenced from other parts of the schema.
为了调用或产生局部过程，必须是从本地过程定义可见的上下文中。
To call or spawn a local process, it must come from a context where the definition of the local process is visible.
上下文定义有局部属性和局部过程定义。
The context definition has local properties and local process definitions.
局部定义的元素只能出现在定义它们的元素定义上下文中。
Locally defined elements can only appear in the context of the element definition in which they are defined.
在清单2中(使用局部声明)，不能在另一个上下文中重复使用movie元素。
With Listing 2 (local declarations), it is impossible to reuse the movie element in another context.
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.
该算法主要包括三个特色技术：基于纹线局部走向的分类预测、体现指纹微观纹理的扩展上下文以及基于成像仪器的分类熵编码器概率模型初始化。
There are mainly three distinguishing features in our proposed algorithm:local direction-based prediction, extended context for micro texture and histogram initialization based on imaging apparatus.
通过上下文的相互作用将局部成分整合成一个有意义的全局特征并从背景中突出。
Local elements are grouped into a meaning global feature according to contextual information, thus allowing them emerge from their backgrounds.
该方法改变了静态利用本体的方式，考虑资源重用的局部性，将来自于大型本体的上下文相关的知识集合表示为子本体。
This approach changed utilization of static ontology and represented context-specific sets of knowledge from large-scale ontology as sub-ontology by considering the locality of resource reuse.
该方法改变了静态利用本体的方式，考虑资源重用的局部性，将来自于大型本体的上下文相关的知识集合表示为子本体。
This approach changed utilization of static ontology and represented context-specific sets of knowledge from large-scale ontology as sub-ontology by considering the locality of resource reuse.
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