封闭测试

机会是你有一个问你这个伟大称号几个朋友，以及现在这里是你的机会，让他们参与到封闭测试，让他们跟你玩。

Chances are you have a few friends asking you about this great title , well now here 's your chance to get them in to the closed beta and let them play with you .

实验结果显示，该方法封闭测试F测度可达63.16%以上。

Experimental results indicated that the F-Score in close-test could reach 63.16 % .

在对大规模真实语料库的封闭测试中，人名、地名和机构识别的F-1值分别达到92.55%、94.53%、86.51%。

In the test on large realistic corpus , its F-1 measure of person name , location name and organization name was 92.55 % , 94.53 % and 86.51 % .

结果表明，组合型切分歧义消解的精确率比ICTCLAS系统有了进一步提高，封闭测试精确率在99%以上，开放测试的精确率为87.84%。

The results show that the rate of correctly disambiguating covering word segmentation ambiguity is over 99 % in the closed test and 87.84 % in the open test .

使用不同的标注语料进行音译单元对齐的封闭测试和开放测试。

We carry on open and close test on machine transliteration unit alignment .

先在不同规模的语料下分别做了一级封闭测试和一级开放测试。

First , corpus of different sizes is made under a closed test and an open test .

然后当词性标记集为二级和三级标记集时分别做了封闭测试和开放测试。

Then , a closed set test and open test are respectively marked when part-of-speech tagging set are 2 and 3 .

封闭测试和开放测试的正确率分别是947%和812%。

In our experiments the algorithm obtains precisions of 94.7 % and 81.2 % respectively for closed test and open test .

在《人民日报》1月份语料库上进行的封闭测试和开放测试中，该方法的标注准确率分别为98.56%和96.60%。

Closed and open tests conducted on People Daily dataset obtain POS tagging accuracies of 98.56 % and 96.60 % , respectively .

初步实验结果显示，该模型的界定准确率为9324%（封闭测试）和9256%（开放测试）。

The preliminary results show that the precision is 93.24 % ( close testing ) and 92.56 % ( open testing ) respectively .

在此基础上，我们经人工标注，建立了流行语释义信息提取的训练语料库，并分别对2004年及2005年的流行语释义信息进行自动提取的封闭测试和开放测试。

Then we go on close test and open test that abstract interpretative information automatically to popular word for 2004 and 2005 separately .

将交叉覆盖算法作为一种分类算法来进行中文文本分类，取得了不错的效果，在封闭测试中的准确率达到98.32%。

This paper introduces alternative covering algorithm to categorize Chinese texts , good effects are obtained and exactness reaches 98.32 % in close tests .

在封闭测试和开放测试中，都取得了较好的实验结果，以人工分词文本为输入底本，调和平均值分别达到了96.33%和94.96%。

Using the manual segmented text as input , F-score of our method achieved 96.33 % in the close test and 94.96 % in the open test .

应用规则模型进行地点实体抽取实验，取得了较好的效果：于封闭测试中获得85.8%的精确率，开放测试获得79.7%的精确率。

The experiment of location entities extraction by rules obtains good result : gets precision of 85.8 % in closet test and 79.7 % in open test .

试验结果显示：该模型的识别正确率达到了86.5%（封闭测试）和77.7%（开放测试），取得了令人满意的结果。

Experimental results demonstrate a high rate of success for predicting boundary location ( 86.5 % correct rate for close testing and 77.7 % for open testing ) .

在哈工大标准问句集上进行实验，取得了语义角色标注封闭测试91.4%，开放测试71.6%的正确率。

Experiment on HIT 's ( Harbin Institute of technology ) question collection , we achieve the 91.4 % precision in close test and 71.6 % in open test respectively .

封闭测试识别精确率达99.01%，召回率达96.67%；开放测试识别精确率达98.14%，召回率达96.19%。

The experiments have achieved 99.01 % precision rate and 96.67 % recall rate in close test , and 98.14 % precision rate and 96.19 % recall rate in open test .

分别对50万汉语语料做封闭测试和开放测试，结果显示，校对后语料的兼类词词性标注正确率分别可提高1132%和597%。

According to the results of close-test and open-test on the corpus of 500,000 Chinese characters , the accuracy of multi-category words'part-of-speech tagging can be increased by 11.32 % and 5.97 % respectively .

在对由2386个实例构建的模板库分别进行句子级的封闭测试及组块级的开放测试，准确率分别在94.98%及94.85%以上。

The close test on sentence level and open test on chunk level based on the templates database builded on 2386 sentences show promising results : the precisions are above 94.98 % and 94.85 % .

测试结果表明，句首的封闭测试精确率和召回率分别为91.06%和94.07%，开放测试精确率和召回率分别为82.13%和85.05%。

As far as the head of the clause is concerned , a result of 91.06 % precision and 94.07 % recall is obtained for the closed test and the open test result is 82.13 % precision and 85.05 % recall .

实验结果表明，识别正确率在封闭测试中可达93.52%，在开放测试中达到77.529%，证明该算法对短语识别问题具有良好的适应性和实用性。

The experimental results show that the precision rates of the phrase recognition in the closed test and the open test are 93.52 % and 77.529 % respectively , which proves that the algorithm has a better adaptability and practicability for phrase identification .

何时打算进行封闭性测试？

Q1.when is the closed beta test ?

封闭语料测试，词性自动标注正确率达95%。

The accuracy of tagging pain of sped is 95 % for closed - corpora .

最后利用上述结论进行了封闭式测试和开放式测试。

Based on the conclusions mentioned above , the authors conduct closed and open tests , respectively .

这种切分算法结合经典n元模型以及EM算法，在封闭和开放测试环境中分别取得了比较好的效果。

The method combining n-gram model and EM algorithm achieves a good effect in the close and opening test .

对真实语料进行封闭和开放测试，封闭测试结果为召回率93.55%，精确率94.14%，F-1值93.85%；

Close and open tests were conducted on real corpus : the close test results are the recall-back rate 93.55 % , accurate rate 94.14 % , F-1 value 93.85 % ;

加载器是机械封闭试验台中测试减速器的重要装置。

The loader is an important apparatus in the circle-mechanical test-bed .

随着中国2001年中国正式成为世贸组织的成员国，国内石油测试服务市场将在三年内向国外公司开放，这对于长期以来封闭的国内测试市场带来了前所未有的机遇和挑战。

With the China entry into WTO in 2001 , the domestic well test service market will open to foreign corporation in the following three years , which will bring about the unprecedented opportunity and challenge for the long-term closed domestic market .

通过对一千句的真实文本进行封闭和开放测试，词性标注的正确率在95%左右，韵律短语切分的召回率在60%左右，正确率达到了80%。

Through close testing and open testing on about 1000 sentences , the correct POS tagging rate is about 95 % , the recalling rate of prosodic phrasing is around 60 % , and the correct rate of prosodic phrasing is about 80 % .

动态分析包括将Web应用程序作为一个封闭的实体进行测试，通过它的官方干涉（主要是HTTP）进行相互作用。

Dynamic analysis involves testing a Web application as a closed entity , interacting with it through its official interfaces ( primarily HTTP ) .