Overview - OmniFind processing and custom text analysis.
概述——omnifind处理和定制的文本分析。
The CPAN Lingua modules can give you power and flexibility in any text analysis task.
CPANLingua模块可以在任何文本分析任务中为您提供功能和灵活性。
For more complex text analysis tasks, the development of a custom annotator may be of interest.
对于更复杂的文本分析任务，可能需要开发定制的注释器。
Many additional text analysis components including word splitting, regex and sounds-like filters.
多个新增的文本分析组件，包括分词、正则表达式过滤器和近似读音过滤器。
This section describes how to create the skills report that displays the results of the text analysis.
本节介绍如何创建显示文本分析结果的技能报告。
The output of text analysis consists of the original text along with additional metadata about the text.
文本分析的输出由原始文本和关于文本的附加元数据组成。
To have the collection use the auto-classifier, it needs to be associated with the new text analysis engine.
为了让集合使用自动分类器，需要将集合与新的文本分析引擎关联起来。
It also described some possibilities regarding how text analysis results can be used to create new insights.
还讨论了通过文本分析发现新信息的一些可能性。
This lets you extend the text analysis capabilities of InfoSphere Warehouse to meet your specific requirements.
这允许您扩展InfoSphereWarehouse的文本分析功能，以满足特定的需求。
Additional text analysis would enable us to find other relations between people, based on statements in the text.
其他文本分析使我们能够根据文本中的语句寻找人们之间的其他关系。
You must specify which are the text fields that you want to run through text analysis (natural language processing).
您必须指定要在哪些文本字段上运行文本分析(自然语言处理)。
This article is the third and last of this three part series about the use of InfoSphere Warehouse for text analysis.
本文是关于使用InfoSphere Warehouse进行文本分析的系列文章的第3部分，也是最后部分。
In one of our projects, there was need for unstructured text analysis; UIMA looked like doing exactly what was needed.
我们曾经有一个项目，它需要非结构化的文本分析；uima似乎是理想之选。
Some of the most interesting and active fields related to text analysis are scientific research and sentiment detection.
与文本分析相关的最有趣、最活跃的领域是科学研究和情感探测。
The rest of this article briefly presents the basic architecture of InfoSphere Warehouse and its text analysis features.
本文的其余部分简要介绍InfoSphereWarehouse及其文本分析特性的基本体系结构。
You can also use custom Apache UIMA compliant text analysis components to implement more advanced text analysis solutions.
您还可以使用定制的ApacheUIMA文本分析组件实现更高级的文本分析解决方案。
In this section, you will create a Cognos report that displays the text analysis results you extracted in the last section.
在本节中，您将创建一个Cognos报告，显示您从上一小节中提取的文本分析结果。
Although T-SQL is extremely powerful for most data processing, it provides little support for text analysis or manipulation.
尽管T - SQL对多数数据处理而言极其强大，但它对文本分析或操作所提供的支持却很少。
Semantic search requires text analysis components to analyze documents for concepts of relevance and create searchable metadata.
语义搜索要求文本分析组件分析文档的概念相关性并创建可搜索的元数据。
In addition to the common text analysis methods above, InfoSphere Warehouse allows the use of Apache UIMA compatible annotators.
除了上面的常用文本分析方法之外，InfoSphere Warehouse还允许使用与Apacheuima兼容的注解器。
But it is easy to consume the result tables containing the text analysis results, like any other database table in the Cognos report.
但是可以很轻松地使用包含文本分析结果的结果表，就如同使用Cognos报告中的任何其他数据库表一样。
An indexing engine, which defines the strategy to use for text analysis (the analyzer) and the index storage to use to record indexes.
索引引擎，用于定义文本分析(分析程序)所用的策略和用于记录索引的索引存储。
By counting the different kinds of words a person says, he is breaking new linguistic ground and leading a resurgent interest in text analysis.
通过计算一个人说话时使用各类单词的数量，他为语言研究开辟了一片新天地，学界对语篇分析的热情卷土重来。
Instead of creating the dictionary from scratch using frequent terms, use the sample it_skills dictionary from the text analysis sample project.
与使用常用词从头创建字典相反，可以使用文本分析样例项目中的样例it_skills字典。
In any text analysis, the first step is to generate a corpus from the textual content, with the subsequent analysis being applied to the corpus.
任何文本分析中，第一步都是从文本内容生成一个语料库(corpus)，后续的分析将应用于此语料库。
It also explains how you can use InfoSphere Warehouse to import and execute text analysis engines that were created with prior versions of UIMA.
此外，还解释了如何使用InfoSphereWarehouse导入并执行使用以前版本的UIMA创建的文本分析引擎。
While this article has focused on text analysis, UIMA can also be used to analyze other kinds of unstructured information such as audio and images.
尽管本文主要关注文本分析，但是UIMA还可以用于分析其他类型的非结构化信息，比如音频和图像。
As described in the previous articles of this series, InfoSphere Warehouse provides tooling for dictionary and regular expression-based text analysis.
如本系列的前两篇文章所述，InfoSphereWarehouse为基于词典和正则表达式的文本分析提供工具。
The additional text analysis performed by the regular expression annotator does have a performance impact and will reduce the maximum parser throughput.
由正则表达式注释器执行的额外文本分析会影响性能，会降低解析器的最大吞吐量。
A general and common type that is often used in text analysis to derive additional types is the Annotation type, which is provided by the UIMA framework.
在文本分析中，用于派生其他类型的概括性的通用类型被称为annotation类型，它由UIMA框架提供。
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