Merge branch 'master' into jiangyufan

Section02-Words-Trees-Probs/section02.test.tex

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-\setbeamertemplate{navigation symbols}{\begin{CJK}{GBK}{hei} 第二章 词法、语法及概率思想基础 \hspace*{2em} 肖桐\&朱靖波 \end{CJK} \hspace*{2em} \today \hspace*{2em} \insertframenumber{}/\inserttotalframenumber}
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-\begin{document}
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-\title{\Large{词法、语法及概率思想基础}}
-\author{\large{\textbf{肖桐\ \ 朱靖波}}}
-\institute{
-\blue{\url{xiaotong@mail.neu.edu.cn}} \black{} \\
-\blue{\url{zhujingbo@mail.neu.edu.cn}} \black{} \\
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-东北大学 自然语言处理实验室 \\
-\blue{\underline{\url{http://www.nlplab.com}}} \black{} \\
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-\maketitle
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-\setlength{\leftmargini}{1em}
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-\section{中文分词}
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-%%% 进一步扩展，基于n-gram LM的方法
-\begin{frame}{进一步扩展：基于$n$-gram语言模型的方法}
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-\begin{itemize}
-\item 这种方法也被称作基于1-gram(统计)语言模型的方法\\
-      所谓统计语言模型就是计算$\textrm{P}(w_1 w_2 ... w_m)$的概率
-\end{itemize}
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-{\scriptsize
-\tabcolsep 5pt
-\begin{tabular}{l | l | l l l}
-链式法则 & 1-gram & 2-gram & ... & $n$-gram \\
-$\textrm{P}(w_1 w_2 ... w_n)=$ & $\textrm{P}(w_1 w_2 ... w_n)=$ & $\textrm{P}(w_1 w_2 ... w_n)=$ & ... & $\textrm{P}(w_1 w_2 ... w_n)=$ \\
-$\textrm{P}(w_1) \times$ & $\textrm{P}(w_1) \times$ & $\textrm{P}(w_1) \times$ & ... & $\textrm{P}(w_1) \times$ \\
-$\textrm{P}(w_2|w_1) \times$ & $\textrm{P}(w_2) \times$ & $\textrm{P}(w_2|w_1) \times$ & ... & $\textrm{P}(w_2|w_1) \times$ \\
-$\textrm{P}(w_3|w_1 w_2) \times$ & $\textrm{P}(w_3) \times$ & $\textrm{P}(w_3|w_2) \times$ & ... & $\textrm{P}(w_3|w_1 w_2) \times$ \\
-$\textrm{P}(w_4|w_1 w_2 w_3) \times$ & $\textrm{P}(w_4) \times$ & $\textrm{P}(w_4|w_3) \times$ & ... & $\textrm{P}(w_4|w_1 w_2 w_3) \times$ \\
-... & ... & ... & ... & ... \\
-$\textrm{P}(w_m|w_1...w_{m-1})$ & $\textrm{P}(w_m)$ & $\textrm{P}(w_m|w_{m-1})$ & ... & $\textrm{P}(w_m|w_{m-n+1} ... w_{m-1})$ \\
-\end{tabular}
-}
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-\begin{itemize}
-\item<2-> \textbf{$n$-gram语言模型}的核心思想就是当前词($w_m$)出现的概率只依赖于前$n-1$个词($w_{m-n+1} ... w_{m-1}$)
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-\vspace{-2em}
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-\begin{eqnarray}
-&   & \textrm{P}_{2\textrm{-gram}}(\textrm{'确实/现在/数据/很/多'}) \nonumber \\
-& = & \textrm{P}(\textrm{'确实'}) \times \textrm{P}(\textrm{'现在'$|$'确实'}) \times \textrm{P}(\textrm{'数据'$|$'现在'}) \times  \nonumber \\
-&   & \textrm{P}(\textrm{'很'$|$'数据'}) \times \textrm{P}(\textrm{'多'$|$'很'}) \nonumber
-\end{eqnarray}
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-\vspace{-1em}
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-\item<2-> \textbf{训练} - 相对频率估计：$\textrm{P}(\textrm{'现在'$|$'确实'}) = \frac{count(\textrm{'确实 现在'})}{count(\textrm{'确实'})}$
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-\end{itemize}
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-\subsection{统计思想}
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-\subsection{统计建模实例1：基于全切分的概率分词}
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-\end{document}