针对训练数据来源的多样化，提出了基于多模板隐马尔可夫模型的文本信息抽取算法。
This paper proposes a new algorithm using hidden Markov model for information extraction based on multiple templates due to the variety of training data.
本研究抽取451名中学生为被试，采用SPSS和结构方程模型分析的方法对数据进行处理。
In this study, 451 middle school students were tested, by using SPSS and structural equation model analysis method for data processing.
提出了基于数据抽取器的知识发现模型。
In this paper, a knowledge discovery model based on data extractor is proposed.
之后介绍了源代码向抽象数据模型的转化过程，重点阐述了代码抽取模式和系统源代码分析过程；
Then introduced the transformation process that from the source code to the abstract data model. Focus on the code extraction patterns and the analysis of the system source code process.
第三部分论述了系统数据仓库的设计，包括数据模型的建立，数据的抽取，以及数据管理。
Part three discussed the design of data warehouse, which included the construction of data model, data extract and data management.
其中重点介绍了多维数据模型的维表、事实表的结构设计，分析了数据模型的构建、数据抽取工具和数据维护工具的设计及实现。
Specially, the paper presented the creating of dimension table, fact table of multi-dimension data model, analyzed the construction of data model, data extraction and data maintenance tools.
该文给出了数据抽取过程中需要的基本定义，描述了数据抽取所基于的页面生成模型。
In this paper, a basic definition of the data extraction process has been given and Described a page generation model of the data extraction.
通过类型定义、特征表示、数据标注、模型训练、实验验证等一系列过程，最终的结果表明能够对论坛数据实现高性能的答案抽取。
After class definition, feature extraction, data annotation, model training and experimenting, the output proves that acceptable performance of answer extraction could be reached.
流程挖掘的目的就是从日志数据中抽取信息构建商业流程执行时的模型，从而能跟踪改进流程。
Process mining aims at extracting information from logs to build up business processes models as they are being executed, and use them to monitor and improve business processes.
流程挖掘的目的就是从日志数据中抽取信息构建商业流程执行时的模型，从而能跟踪改进流程。
Process mining aims at extracting information from logs to build up business processes models as they are being executed, and use them to monitor and improve business processes.
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