组块

首先明确了中文组块的定义，cotraining算法的形式化定义。

Firstly , we give the definition of Chinese chunk , then the formalized definition of co-training algorithm .

基于SVM的中文组块间依存关系分析

Chinese Chunk Dependency Analysis Based on Support Vector Machines

基于SVM的组块识别及其错误驱动学习方法

Chunk Parsing Based on SVM and Error-Driven Learning Methods

SVM和基于转换的错误驱动学习相结合的汉语组块识别

SVM-Based Chinese Chunk Recognition and Transformation-Based Error-Driven Learning

基于SVM的句子组块识别

Chunk parsing for sentences based on SVM

基于Stacking算法的组合分类器及其应用于中文组块分析

Combined Multiple Classifiers Based on a Stacking Algorithm and Their Application to Chinese Text Chunking

我们利用企业tier中的业务组块实现了企业应用集成中的简单性，和可管理性。

Using the business component in the enterprise tier , we attained the simplicity and manageability .

本文将中文组块识别问题看成分类问题，并利用SVM加以解决。

In this paper , we treat Chinese text chunking as a classification problem , and apply SVM to solve it .

在引入错误驱动学习方法后，两种模型组块识别结果的F值分别提高了1.05%和0.66%。

With the help of error-driven learning , the performances of Specialized HMM-based chunking and SVM-based chunking are improved by 1.05 % and 0.66 % .

手运动组块设计与事件相关设计的fMRI比较研究

A Control Study of Hand Motor fMRI Experiment with Blocked Design and Event Related Design

为进一步提高组块识别的结果，采用错误驱动学习机制分别对增益HMM模型和SVM模型的识别结果进行校正。

Moreover , an error-driven learning approach is adopted to improve the chunk parsing results of Specialized HMM and SVM model .

我们使用ECA规则来驱动业务组块中的服务交互。

We use EGA rules to drive the service interaction in the business component .

分布式策略与CRFs相结合识别汉语组块

A Distributed Strategy for CRFs Based Chinese Text Chunking

通过组块分析对“ilst”原子。iTunes用ilst来存储元数据，它和ID3是等价的。

Partial parsing support for the'ilst'atom which is the ID3 equivalent iTunes uses to store meta data .

对于SVM模型，选择组块的多种不同特征信息组合和不同的多分类划分方法，训练学习后得到了基于统计的SVM模型。

Via the analysis of the characteristic information from the chunks which have been tagged , we choose the different combination of characteristic information and classification means to realize the SVM models .

通过将不同的上下文信息导入隐马尔可夫模型（HiddenMarkovmodel，HMM）中，构建了5个二元增益HMM模型用于汉语句子的组块识别。

Two systems for chunk parsing are built based on the Specialized Hidden Markov Model and Support Vector Machine Model . According to the different contextual information , we build five Specialized HMMs for Chinese chunk parsing .

采用半指导机器学习方法Co-training实现中文组块识别。

In this paper we discuss the application of semi-supervised machine learning method & Co-Training on Chinese Text Chunking .

在满足实时性要求前提下，实现了飞行管理系统中控制显示组块（CDU）的动态图形仿真。

Subject to the need of real time , the dynamic graphic simulation for Control Display Unit ( CDU ) in flight management system has been realized .

特别是词汇组块突破了传统意义上的词汇及搭配的范围，已经扩大到语句甚至语篇的范畴，有利于语篇理解能力的培养（Lewis，1993）。

In particular , lexical chunks break through the traditional sense of the word and collocation , whose scope has been expanded to sentence or even discourse , which are conducive to discourse comprehension abilities ( Lewis , 1993 ) .

结论如下：（1）随着记忆负荷的增加，两类组块加工水平都出现显著下降，验证了集大小效应（Set-SizeEffect）。

The conclusions indicate that : 1 . With the increase of memory load , the level of processing of the two new chunk have a remarkable decline which verify the effect of Set-size effect . 2 .

M-Ph模型认为自然语言的基本组块M具有符号性，用于指代或者诱发心理实体。

The model " M-Ph " regards the basic block M of a natural language as semiotic characteristics .

引入错误驱动的N-fold模板纠正后处理算法进行后处理，进一步提升分析模型的性能。第三，对于组块分析模型中的特征选取问题进行研究。

N-fold template correction post-processing algorithm was introduced for further improving the performance . Thirdly , the research on the features selection in the chunking model brought some important issues .

采用GE3.0THDx超导型磁共振扫描仪对所有被试者进行全脑高分辨力解剖像、静息态及组块设计的单手虚握拳运动任务态fMRI扫描。

GE3.0T HDX MR Scanner was used to obtain high resolutions anatomy images and fMRI data in resting and task state , which was block-designed fMRI with fisting of each hand .

现在组块分析广泛用于自然语言处理的众多方面，尤其是在基于实例的机器翻译EBMT研究中，组块分析是重要技术之一。

Now , chunk identification is widely used in many fields of natural language processing , especially in the example based machine translation ( EBMT ), in which chunk identification is one of major techniques .

本文采用半指导的机器学习方法Co-training进行中文组块识别的研究，在论文中，我们定义了中文组块的定义，在可能近似正确模型（PAC）的框架下讨论了Co-training方法的形式化定义。

In this paper we build a research work on the recognition of Chinese chunk with the Co-training method . We give the definition of Chinese Chunk , then discuss formalized definition of Co-training algorithm under the PAC framework .

简要介绍在塘沽基地8000T级组块滑道工程大直径超深灌注桩施工过程中的关键技术。

This paper briefly introduced the key technique in the construction process of big-diameter and super-depth Cast-in-Situ pile for the8000T block slipway of Tanggu basement .

首先，采用分而治之的方法将各组块进行分组，为各分组分别选取合适的单一特征和组合特征，用CRFs进行组块识别；然后将识别结果加入到特征模板中，进行CRFs的第二次识别。

Firstly , divide the chunks into groups , with the method of divide-and-conquer , then select appropriate single and combined features for each group respectively , and implement chunk recognition based CRFs ; Results of the former recognition are added to the second identification template .

大型海洋石油平台组块滑移装船过程中滑道结构安全性评价

Security evaluate of skidway structures of large offshore platforms loadout process

汉语自然话语韵律组块的优选论分析

An OT Analysis of the Prosodic Chunking of Chinese Spontaneous Speech

实验结果显示，该方法在文本组块分析方面是有效的。

The experiment results show that it is an effective approach .