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Grzegorz Chrupała committed d39019d

More content in slides

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doc/clin-2012/lda-plate.pdf

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doc/clin-2012/slides.tex

 \documentclass[17pt]{beamer}
 \usefonttheme[onlymath]{serif}
 \usepackage{latexsym,amsmath,url}
+\usepackage{pifont}
 \usepackage{hyperref}
 \usepackage{color}
 \usepackage[utf8]{inputenc}
 \usepackage{framed}
+
 \DeclareSymbolFont{AMSb}{U}{msb}{m}{n}
 \DeclareMathOperator*{\argmax}{argmax}
+\newcommand{\tick}{\color{blue}\ding{51}}
+\newcommand{\cross}{\color{red}\ding{55}}
+
+
 \mode<presentation>{ 
   \usetheme{Boadilla}
 } \title[Word class learning]{Word class learning}
 \subtitle{Computational and cognitive aspects}
 
 \date[CLIN 2012]{CLIN 2012}
-\author[GC, AA, YM]{Grzegorz Chrupa{\l}a \and Afra Alishahi \\ \and Yevgen Matusevych}
+\author[Chrupa\l a...]{Grzegorz Chrupa{\l}a \and Afra Alishahi \\ \and Yevgen Matusevych}
 
 \institute[UdS, UvT]
 %{
 \frame{\titlepage}
 
 \begin{frame}\frametitle{Word classes}
-  \begin{block}{}
+  \begin{block}{}\small
     \begin{center}
       \begin{tabular}{c}
-        \color{red} Berlin  Bangkok  Tokyo  Warsaw\\
-        \color{blue} Sarkozy  Merkel Obama  Berlusconi \\
-        \color{olive} Mr  Ms  President  Dr \\
+        \color{red} go come fit try hang read say take see blow \\
+        \color{blue} bricks bits food things medicine cream \\
+        \color{olive} the your that this a my his some\\
       \end{tabular}
     \end{center}
-
   \end{block}
 
+  \begin{block}{}\small
+    \begin{center}
+      \begin{tabular}{c}
+        \color{magenta} Berlin  Bangkok  Tokyo  Warsaw\\
+        \color{violet} Sarkozy  Merkel Obama  Berlusconi \\
+        \color{orange} Mr  Ms  President  Dr \\
+      \end{tabular}
+    \end{center}
+  \end{block}
   \begin{itemize}
   \item Groups of words sharing syntax/semantics
   \item Useful for generalization and abstraction
   \end{itemize}
 \end{frame}
 
+\begin{frame}
+  \frametitle{Perspectives on class learning}
+  \begin{itemize}
+  \item NLP
+    \begin{itemize}
+    \item Efficiency
+    \item Performance on NLP tasks
+    \end{itemize}
+  \item Cognitive modeling
+    \begin{itemize}
+    \item Plausible cognitive constraints 
+    \item Performance on simulations of human tasks
+    \end{itemize}
+  \end{itemize}
+\end{frame}
+
+
+
+\begin{frame}
+\frametitle{Goals}
+  \begin{itemize}
+  \item Bring two perspectives closer together
+  \item Analyze and improve 2 algorithms
+    \begin{itemize}
+    \item {\color{blue}$\Delta$H} - simulate online learning of word
+      classes by humans (Chrupa{\l}a and Alishahi 2010)
+    \item {\color{blue}Word class LDA} - efficiently learn soft word
+      classes for NLP (Chrupa{\l}a 2011)
+    \end{itemize}
+  \end{itemize}
+\end{frame}
+
+
+\begin{frame}
+  \frametitle{Brief comparison}
+  \begin{center}
+    \begin{tabular}{l|c|c}
+      & $\Delta$H     & cLDA \\ \hline
+      Token level     & \tick     & \tick \\
+      Soft classes    & \tick     & \tick \\\hline
+      Bayesian        & \cross    & \tick \\
+      \bf Online      & \tick     & \cross \\
+      Unbounded       & \tick     & \cross \\
+      \bf Parameters  & \cross    & \tick \\ 
+      Fast            & \cross    & \tick \\
+    \end{tabular}
+  \end{center}
+\end{frame}
+
+\begin{frame}
+  \frametitle{$\Delta$H}
+  \begin{itemize}
+  \item Incrementally optimizes a joint entropy criterion:
+    \begin{small}
+      \begin{equation*}
+        H(X,Y) =  {\color{red} H(Y)} + {\color{blue} H(X|Y)} 
+      \end{equation*}
+    \end{small}
+    \begin{itemize}
+    \item Small {\color{red}class entropy} - parsimony
+    \item Small {\color{blue} conditional feature entropy} -
+      informativeness
+    \end{itemize}
+  \item New classes are created as needed
+  \item No free parameters
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Word class LDA}
+  \begin{itemize}
+  \item \small Generative model equivalent to LDA for topic models
+  \end{itemize}
+  \begin{center}
+    \includegraphics[scale=0.25]{lda-plate.pdf}
+  \end{center}
+\end{frame}
+
 \end{document}