先看一下语言模型的输出格式 \data\ ngram = ngram = ngram = \-grams: -5.24036 'cause -0.2084827 -4.675221 'em -0.221857 -4.989297 'n -0.05809768 -5.365303 'til -0.1855581 -2.111539 </s> 0.0 - <s> -0.7736475 -1.128404 <unk> -0.8049794 -2.271447 a -0.616

关于n-gram 语言模型,大部分在这篇博客里 记过了,   SRILM 语言模型格式解读 , 其实看完了,ngram的大概用法都比较清楚了, 但是关于平滑算法,一直很模糊,就晓得一个"劫富济贫" ,也不知 回退 ,插值,折扣,平滑,都说的什么东西,模模糊糊的,找了很多资料,还是看官方文档吧,看具体公式,就明白了.   看全部翻译 参考 :   Ngram 折扣平滑算法 ,本文里夹带着自己的一些理解.  本文档翻译自 srilm 手册ngram-discount.7.html NAME

在学习NLP过程中,遇到了Good-turning的介绍,网上找了相关的资料查看,总结如下. 思想: 其主要思想是从概率的总量中分配少量的比例给零概率项. 思路: 假定给定的语料库中出现 \(r\)次数的词有\(N_r\)个,则有 \[N = \sum_{i=1}^{\infty }rN_r \] 当\(r\)较小时,我们使用\(d_r\)代替\(r\),这里\(d_r < r\),\(d_r\)的值可有下式表示 \[d_r = ( r+1 )N_{r+1}/N_r \] 其期望可以表示为 \[

SRILM是一个建立和使用统计语言模型的开源工具包,从1995年开始由SRI 口语技术与研究实验室(SRI Speech Technology and Research Laboratory)开发,现在仍然不断推出新版本,被广泛应用于语音识别.机器翻译等领域.这个工具包包含一组C++类库.一组进行语言模型训练和应用的可执行程序等.利用它可以非常方便地训练和应用语言模型.给定一组连续的词,调用SRILM提供的接口,可以得到这组词出现的概率. http://www.jianshu.com/p/5b1

目录: 一基本训练 二语言模型打分 三语言模型剪枝 四语言模型合并 五语言模型使用词典限制 一.基本训练 #功能 读取分词后的text文件或者count文件,然后用来输出最后汇总的count文件或者语言模型 #参数 输入文本: -read 读取count文件 -text 读取分词后的文本文件 词典文件: -vocab 限制text和count文件的单词,没有出现在词典的单词替换为<unk>:如果没有,所有的单词将会被自动加入词典 -limit-vocab 只限制count文件的单词(对text

1.简介 SRILM是通过统计方法构建语言模型,主要应用于语音识别,文本标注和切分,以及机器翻译等. SRILM支持语言模型的训练和评测,通过训练数据得到语言模型,其中包括最大似然估计及相应的平滑算法:评测是计算测试集的困惑度.其最基础和最核心的模块是n-gram模块,包括两个工 具:ngram-count和ngram,被用来估计语言模型和计算语言模型的困惑度. 2.使用方法 (1).语料初始化 a.数据清洗        b.分词(以空格划分)        c.将数据分为训练集和测试集 (2

http://52opencourse.com/111/斯坦福大学自然语言处理第四课-语言模型(language-modeling) 一.课程介绍 斯坦福大学于2012年3月在Coursera启动了在线自然语言处理课程,由NLP领域大牛Dan Jurafsky 和 Chirs Manning教授授课:https://class.coursera.org/nlp/ 以下是本课程的学习笔记,以课程PPT/PDF为主,其他参考资料为辅,融入个人拓展.注解,抛砖引玉,欢迎大家在“我爱公开课”上一起探讨学

训练n-gram语言模型 ngram-count -text train.txt -order -lm model -kndiscount -interpolate -gt3min -gt4min 计算生成PPL ngram -lm model -order -ppl test.txt -debug 2> srilm.ppl ppl与ppl1有和区别? ppl = 10^(-logprob / (words - OOVs + sentences)) ppl1 = 10^(-logprob / (

官网 网上搜的安装教程:SRILM的安装方法 最近做的一个项目要用到语言模型,在网上找了一些开源的工具包试了一下.废话不多说,下面直接介绍一下SRILM的安装方法. 我实在ubuntu14.04底下使用SRILM. SRILM的下载地址(我使用的是1.7.1版本) 1.安装 tcl 有用SRILM要用到tcl工具的一些功能,所以我先装了tcl. tcl的下载地址(我使用的是 tcl8.6.5-src.tar.gz) 在tcl解压包的unix文件夹中的README文件介绍了tcl的安装方法.其实也

一.产生句子 方法:Shannon Visualization Method 过程:根据概率,每次随机选择一个bigram,从而来产生一个句子 比如: 从句子开始标志的bigram开始,我们先有一个(<s>, w),w是随机一个单词,比较有可能的是I这个单词,那么我们就有(<s>, I) 随机选择下一个单词,得到(w,x),这里w是I,x概率最大的是want 重复以上步骤,直到得到</s> 问题1:过度拟合.N-grams在预测句子上只有当测试语料库和训练语料库非常相似

一.语言模型 旨在:给一个句子或一组词计算一个联合概率 作用: 机器翻译:用以区分翻译结果的好坏 拼写校正:某一个拼错的单词是这个单词的概率更大,所以校正 语音识别:语音识别出来是这个句子的概率更大 总结或问答系统 相关任务:在原句子的基础上,计算一个新词的条件概率,该概率与P(w1w2w3w4w5)息息相关. 任何一个模型计算以上两个概率的,我们都称之为语言模型LM. 二.如何计算概率 方法:依赖概率的链式规则 从而有: 问题:如何预估这些概率 方法一:计数和细分 但是不可能做到! 原因:句子

今天这篇博客就聊聊几种常见的查找算法,当然本篇博客只是涉及了部分查找算法,接下来的几篇博客中都将会介绍关于查找的相关内容.本篇博客主要介绍查找表的顺序查找.折半查找.插值查找以及Fibonacci查找.本篇博客会给出相应查找算法的示意图以及相关代码,并且给出相应的测试用例.当然本篇博客依然会使用面向对象语言Swift来实现相应的Demo,并且会在github上进行相关Demo的分享. 查找在生活中是比较常见的,本篇博客所涉及的这几种查找都是基于线性结构的查找.也就是说我们的查找表是一个线性表,我

一.背景 近期研究了一下语言模型,同事推荐了一个比较好用的工具包kenlm,记录下使用过程. 二.使用kenlm训练 n-gram 1.工具介绍:http://kheafield.com/code/kenlm/ 2.工具包的下载地址:http://kheafield.com/code/kenlm.tar.gz 3.解压后运行,./bjam 进行编译 4.使用如下命令进行训练:bin/lmplz -o 5 --verbose_header --text data/chat_log.txt --ar

Mittag-Leffler定理    设$D\subset\mathbb C$为区域,而$\{a_{n}\}$为$D$中互不相同且无极限点的点列,那么对于任意给定的一列自然数$\{k_{n}\}$,定义函数$$\psi_{n}(z)=\sum_{j=1}^{k_{n}}\frac{c_{n,j}}{(z-a_{n})^j},n\in\mathbb N$$ 则必存在$D$上的亚纯函数$f(z)$使得$f$以$\{a_{n}\}$为其极点集,且在每个$a_{n}$附近的Laurent展开式的主要部

插值查询:如果有这样一张表,有一列叫水位,有一列叫库容,比如下面的图. 我现在想做这么一件事情:对于这个测站而言,当我输入某一个水位或者库容的时候,想要查询到对应的水位或者库容呢? 而这个值不一定是存在数据库中的,也许这只是一个推导出来的近似值呢? 算法要点:如果这个输入的值是位于数据库值的某一个区间内的话,那么取最小的区间,然后求这个区间内单位数量的值. 大家听得可能有点不太明白,我画张图. 呵呵,应该有点眉目了吧?这个第一步也是最重要的一步就是确定区间哦 算法的话很简单,用一句公式来概括就是

主要两个方面 Probabilistic modeling 概率建模,神经网络模型尝试去预测一个概率分布 Cross-entropy作为误差函数使得我们可以对于观测到的数据 给予较高的概率值 同时可以解决saturation的问题 前面提到的线性隐层的降维作用(减少训练参数)     这是一个最初版的神经网络语言模型     选取什么要的loss function,为什么用cross-entropy,为什么不用squared loss呢? 首先 可以看到 cross-entropy更能从数值上体

在AngularJS中显示模型中的数据有两种方式: 一种是使用花括号插值的方式: <p>{{text}}</p> 另一种是使用基于属性的指令,叫做ng-bind: <p ng-bind="text"></p> 这两种方式的效果都是一样的,其主要区别在于,使用花括号语法时,在AngularJS使用数据替换模板中的花括号时,第一个加载的页面,通常是应用中的index.html,其未被渲染的模板可能会被用户看到.而使用第二种方法的视图不会遇到这

一.发展 起源:统计语言模型起源于 Ponte 和 Croft 在 1998年的 SIGIR上发表的论文 应用:语言模型的应用很多: corsslingual retrieval distributed IR expert finding passage retrieval web search genomics retrieval 基因组学检索 topic tracking subtopic retrieval 二.basic model 1.Ponte and Croft 核心思想:quer

项目里面有用到插值滤波器的场合,用matlab做了前期的滤波器性能仿真,产生的滤波器系数保存下来输入到FPGA IP中使用即可. 下面是仿真的代码 % clear all close all Nx = ; Tx = ; nx = :Nx-; x = sin(*pi**nx/Tx); L = ; % Ny = L * Nx; % ny = :Ny-; % yi = zeros(,Ny); % yi(:L:Ny) = x; % figure; % stem(yi); Fst1 = 0.05; Fp1

首先呢,插值语法也就是{{}}和ng-bind基本上是没有区别的. 主要区别在于,使用花括号语法时,在AngularJS使用数据替换模板中的花括号时,第一个加载的页面,通常是应用中的index.html,其未被渲染的模板可能会被用户看到.而使用ng-bind方法不会遇到这种问题. 原因是,浏览器需要首先加载index.html页面,渲染它,然后AngularJS才能把它解析成你期望看到的内容. 所以,对于index.html页面中的数据绑定操作,建议采用ng-bind.那么在数据加载完成之前用户