With Gradient Boosting Decision Tree model, adding the user behavior features including the title features, query features, click features to the literal features make accuracy increased by about 25%, recall rate increased by about 24% and F value increased by about 30%.

Compared with other element level matching algorithms in EON2004 competition, LANA has higher accuracy and recall without increasing complexity of matching.

与EON2004中的其它元素级本体匹配方法相比，LANA在没有增加匹配复杂度的基础上，具有较好的准确率和召回率。

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Our experimental results have shown that comparing with the traditional Chart parsing algorithm, our approach can significantly reduce the time consumption and the numbers of ambiguous edges, and get about 7% of the correct rate and the recall rate increasing while parsing long Chinese sentences.

实验证明我们的方法与传统的一遍图表（chart）分析方法相比，能够大大减少时间消耗和歧义边的个数，并且提高了复杂长句分析的正确率和召回率约7%。

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By comparing the results of experiment get the conclusion: in the same experimental condition SVM has better filtering performance index ( including recall rate, accuracy rate, precision rate and F value) than NB in the Chinese spam filtering.

通过比较实验结果可知：在相同的实验条件下，SVM在进行中文垃圾邮件过滤时其过滤性能指标（包括召回率、正确率、准确率和F值）较NB有所提升。

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Compared with N-gram method, the results are satisfying: The performance of precision 82% and recall 75% are reached by proposed approach.

通过与基于N-gram的统计方法比较，结果是令人满意的.精确率达82%，召回率达75%。

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With respect to the precision and recall of clustering, the experiments indicate that the proposed algorithm is with better performance than the standard ant clustering algorithm and the k-means algorithm.

结果证明本文所提出的基于本体的扩展算法在聚类精度和召回率上优于标准的蚁群聚类算法，达到了算法改进的目的。另外通过与K-means算法的结果比较也显示了本文算法的优越性。

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The results show that SVM and Maximum Entropy combined Chinese organization name recognition is effective: in open test, the recall and precision rate and F-measure are 91.05%, 93.59%, and 92.84% respectively.

实验表明，SVM和最大熵相结合的中文机构名自动识别方法是有效的：系统开式召回率和精确率分别达91.05%，93.59%，F值为92.84%。

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According to mandate of the Conference TREC, we used HITS algorithm for comparison. The algorithm proved its efficiency and feasibility by experiment results.

利用HITS算法，根据TREC会议的任务模式对算法进行了比较验证，证明算法在召回率-精度上的表现优于传统算法。

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Finally, this paper builds the SVM construction. By testing 1 000 sentences with the SVM constructed, the ideal result of 69.6% accuracy and 67.8% recall ratio is obtained.

通过对待测的1000句汉语句子应用SVM进行了动词搭配的自动识别分析，获得了开放测试抽取正确率为69.6%，召回率为67.8%的理想结果。

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After adding these five characteristics in turn, the overall accuracy, recall and AUC of the answer are growing.

依次加入上述五大特征后，整体的准确率、召回率和AUC是增长的。

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Post hoc analysis shows that the acquired SCF alternatives reach a precision of 68.37% and a recall of 81.71%.

结果表明，自动获取的SCF句式转换对的精确率为68.37%，召回率达到81.71%。

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In a named entity test organized by the 863 program, the precision, recall and F-score of the system reach 81.93%, 78.20% and 80.02% respectively.

在863组织的命名实体识别评测中，系统的准确率、召回率和F值分别达到了81.93%，78.20%，80.02%。

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And the results show that compared to the knowledge acquisition method supported by semantic Web ontology, this method can acquire not only precise knowledge but also imprecise knowledge, and the recall ratio is improved obviously.

结果表明：相对于语义Web本体支持的知识获取方法，该方法不但能获取到精确知识而且能获取到不精确知识，并且明显提高了知识获取的召回率。

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Finally, do the numerical experiments, and to automatic word extraction indexing accuracy, recall and F integrated indicators and other technical parameters were compared and analyzed.

最后进行了数值实验，并就自动抽词标引实验准确率、召回率和综合指标F等技术参数进行了对比分析。

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The proposed algorithm has already been applied in our Web answering system, the experimental result shows that it has high accuracy and efficiency of word segmentation and elimination of the ambiguity of words segmentation.

该算法已经在基于Web的自然语言答疑系统WebAnswerSystem中实现并得到了实际应用。实验结果表明，算法具有较高的准确率和召回率，对自然语言网络答疑中进行切分歧义消除是行之有效的。

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The practice result proves that, this system has better extraction speed based on the Web information extraction technology research and higher rate of accuracy and recalls rate than the current Web-based information extraction techniques.

实践结果证明，本方案相对于常见的Web信息抽取技术方法具有更好的提取速度以及更高的准确率、召回率。

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Especially, the precision, recall and F value of complicated organization name recognition is increased by 0.24%, 1.39% and 0.86% respectively.

特别是结构比较复杂的机构名识别准确率提高了0.24%，召回率提高了1.39%，F值提高了0.86%。

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When Web pages have 2 comments or more, the Web information extraction method based on semantic tags has a great effect on accuracy rate and recall rate.

当网页中评论数量大于2条以上时，基于语义标记的Web信息抽取方法在准确率和召回率上有很好的效果。

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Word clusters generated by the methods mentioned above are introduced into alignment-based Chinese syntactic induction, improve the performance.

将上述四种方法生成的词类引入无监督的基于对齐的汉语句法结构自动推导后，准确率、召回率以及F值都提高了1%左右。

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Experiment shows the precision is 72.19%, recall is 77.98% and F measure is 74.97%.

实验结果表明，术语抽取正确率为72.19%，召回率为77.98%，F测量为74.97%。

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The experimental results show that both the precision and the recall of our method are better than the main ontology learning tools in Web usage domain.

实验表明，在Web使用领域内，该方法学习生成的应用日志本体在准确率和召回率上都明显优于目前主流本体学习工具生成的结果。

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