查询词典 trigram

The pg_trgm module provides functions and operators for determining the similarity of text based on trigram matching, as well as index operator classes that support fast searching for similar strings.

pg_trgm 模块提供了基于 trigram 匹配的算法计算文本相似性的函数和操作符，以及支持快速查找类似字串的操作符表。

Each trigram consists of three lines. Some lines are continuous and represent the male principle of yang, indicating positive direction, activity, and productivity , and providing heavenly symbols of light, heat, and life.

每一卦由三个爻组成，有些爻是连线，代表阳道，表示积极、主动和生产力，还表示热、明和生命等天堂象征。

Note: The environment of starting divinatory trigram, should be quiet, should be one person with whole heartedness.Rules: 1. predict the first thing you want to predict. 2. One time one thing to predict, cannot predict two things at the same time. 3. Predict yourself not others. 4. Not need to predict again once result comes out.

须知：起卦环境：安静的环境、独自一人、要有诚心原则：一、尽量在想预测某事的第一念头起时，就测；二、一次只能测一件事，不可同时测两件事；三、只自己测，不要测别人；四、若测出结果，不必再测。

His/her the abdomen round drum, the Eight Diagrams trigram symbol is prominent, is symbolizing Chinese nation culture broad and profound.

其腹圆鼓，八卦爻符突出，象征着中华民族文化的博大精深。

In this paper, character trigram is used for analyzing the part of sentence and detecting local errors. Then dependency grammar is introduced into automatic proofreading. Dependency relationship analysis parses the whole sentence and denotes dominating and dominated relation among the words, so it is helpful to detect collocation errors with long distance, efficiently filling up the deficiency of n-gram.

本文使用字的三元模型对文本进行局部的分析与错误查找，同时将依存文法分析应用于自动校对中，由于依存文法对句子进行全局分析，指出了句子中词与词之间的依存关系，所以能够有效的查找出文本中的远距离搭配错误，补充了n元语法的不足。

It constructs Trigram statistic language model based on large-scale corpus,and builds corresponding bintree for the sentence waiting disposal;and then it predicts speech pauses at two different angle using information provided by tree.

基于大规模语料，利用三元模型Trigram，建立统计语言模型；基于SLM为待处理句子生成相应的二叉树；将生成的二叉树所包含的信息，从不同角度应用于语音停顿的预测。

This is the trigram Tui , THE JOYOUS , the Smiling Lake, which stands in the west "and leads the year toward its fruition and joy."

如易经中的"兌"卦，，象征喜乐，微笑的湖，"立于西方，引领一年至丰收和喜悦的季节"。

The hybrid model better describes the relation between semantics and words and achieves a lower perplexity in tagging corpus.In Mandarin speech recognition,this model shows a better performance and requires less memory space than the word based trigram model.

这个模型比传统词义模型更准确地描述了词义和词的关系，在词义标注中具有较小的混淆度；在汉语普通话连续音识别中，这个词义模型的性能优于基于词的三元文法模型，并且需要较小的存储空间

This paper adopts Hidden Markov Model in part-of-speech tagging for English texts.Firstly the authors intro-duce the improvement on Viterbi algorithm and the step in training computer by means of hidden Markov model.Sec-ondly,through a series of contrast experiments the authors come to the conclusions:The Bigram model is better than the Trigram model in terms of the performance-cost ratio.

利用隐马尔可夫模型对英语文本进行词性标注，首先介绍了对Viterbi算法的改进和基于HMM模型方法训练机器的步骤，然后通过一系列对比实验，得出两个结论：二元文法模型的"性能价格比"较三元文法模型更令人满意；词性标注集的个数对词性标注的准确率有影响。

Evaluations on the task of Japanese text input show that:(1) MSR/MSR-II significantly outperforms a traditional trigram model, reducing CER by 20.9%;(2) MSR/MSR-II is comparable to the other two state-of-the-art discriminative algorithms, Boosting and Perceptron;(3) MSR-II outperforms MSR not only in time/space complexity but also in the stability of feature selection;(4) Experimental results of domain adaptation show the robustness of MSR-II.

日文汉字输入实验的评测结果表明：(1) MSR/MSR-II显著优于传统三元模型，使错误率下降了20.9%；(2) MSR/MSR-II与另两类主流判别学习算法Boosting和Perceptron表现相当；(3) MSR-II不仅在时空复杂度上优于MSR，特征选择的稳定性也更高；(4)领域适应性建模的结果证明了MSR-II的良好鲁棒性。

Finally it offers the analysis of the fault-tolerance system as well as its test verification.

这样，运行于空间环境中的系统的设计目标就是在保证系统实时性

However, it is still difficult to find out the real reason of the increase of the keratinized gingiva during the orthodontic extrusion whether it is due to the proliferation of the gingival tissue or to its elastic nature, that is because the proliferation of the fibroblasts as well as the connective tissue changes in volume and the intra-fibers spaces were all immeasurable in most of the studies.

然而，仍然难以找到真正的原因增加的角化牙龈在正畸挤压它是否是由于扩散的牙龈组织或其弹性性质，这是因为增殖的成纤维细胞以及作为结缔组织的变化量和内部纤维空格都是不可估量的，大多数的研究。