1 files changed, 0 insertions, 167 deletions
diff --git a/report/intro_slides/tcohn_slides.tex b/report/intro_slides/tcohn_slides.tex
deleted file mode 100644
index 950a0036..00000000
--- a/report/intro_slides/tcohn_slides.tex
+++ /dev/null
@@ -1,167 +0,0 @@
-\documentclass{beamer}
-
-\mode<presentation>
-{
- \usetheme{Boadilla}
- \setbeamercovered{transparent}}
-
-\usepackage[english]{babel}
-\usepackage{times}
-
-\usepackage{xcolor}
-\usepackage{colortbl}
-%\usepackage{subfigure}
-
-%% for tables
-\newcommand{\mc}{\multicolumn}
-\newcommand{\lab}[1]{\multicolumn{1}{c}{#1}}
-\newcommand{\ind}[1]{{\fboxsep1pt\raisebox{-.5ex}{\fbox{{\tiny #1}}}}}
-\newcommand{\IND}[1]{{\fboxsep1pt\raisebox{0ex}{\fbox{{\small #1}}}}}
-\newcommand\production[2]{\ensuremath{\langle\mbox{#1}, \mbox{#2}\rangle}}
-
-%% markup
-\newcommand{\buffer}[1]{{\color{blue}\textbf{#1}}}
-\newcommand{\pred}[1]{\code{#1}}
-
-%% colors
-\newcommand{\textred}[1]{\alert{#1}}
-\newcommand{\textblue}[1]{\buffer{#1}}
-\definecolor{tablecolor}{cmyk}{0,0.3,0.3,0}
-\newcommand{\keytab}[1]{\mc{1}{>{\columncolor{tablecolor}}d}{#1}}
-
-% rules
-\newcommand{\psr}[2]{#1 $\rightarrow \langle $ #2 $\rangle$}
-
-\newenvironment{unpacked_itemize}{
-\begin{itemize}
-  \setlength{\itemsep}{10pt}
-  \setlength{\parskip}{0pt}
-  \setlength{\parsep}{0pt}
-}{\end{itemize}}
-
-\newcommand{\condon}{\hspace{0pt} | \hspace{1pt}}
-\definecolor{darkblue}{rgb}{0,0,0.6}
-\newcommand{\blueexample}[1]{\textcolor{darkblue}{\rm #1}}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\newcommand{\ws}{\ensuremath{\vec{w}}}
-\newcommand{\pu}{\ensuremath{P_0}}
-\newcommand{\bx}{\mathbf{x}}
-\newcommand{\bz}{\mathbf{z}}
-\newcommand{\bd}{\mathbf{d}}
-\newcommand{\by}{\mathbf{y}}
-\newcommand\bleu{${B{\scriptstyle LEU}}$}
-
-
-\title{Clustering of phrases and contexts}
-\author{Trevor Cohn}
-\date{\today}
-
-\begin{document}
-
-\begin{frame}[t]{Motivation}
-%\vspace{1.0cm}
-\begin{exampleblock}{Distributional Hypothesis}
-\begin{quote}
-\emph{Words that occur in the same contexts tend to have similar meanings}
-\end{quote}
-\hfill (Zellig Harris, 1954)
-\end{exampleblock}
-
-\vspace{3ex}
-
-We will leverage this in a translation setting:
-\begin{itemize}
-    \item Use the contexts to \alert{cluster} translation units into groups
-    \item Units in the same group expected to be semantically and syntactically similar
-    \item Then use these cluster labels to guide translation
-    \begin{itemize}
-        \item lexical selection: translating ambiguous source word/s
-        \item reordering: consistent syntactic patterns of reordering
-    \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}[t]{Monolingual Example}
-Task: cluster words into their parts-of-speech. \\
-
-\vspace{1ex}
-Illustrate by starting with the word `deal' (noun or verb):
-
-\only<1>{\includegraphics[width=\columnwidth]{deal_first.pdf} \\ Step 1: Find contexts for `deal'}
-\only<2->{\includegraphics[width=\columnwidth]{deal.pdf} \\ Step 2: Find other words which occur in these contexts}
-%\only<3>{\includegraphics[width=\columnwidth]{deal_more.pdf} \\ \ldots continue to expand}
-
-\only<3>{
-\vspace{1ex}
-Notice that the instances of deal can be split into two connected sub-graphs:
-\begin{itemize}
-    \item noun: the left two contexts ``a \ldots with'' and ``a \ldots that''
-    \item verb: the right two contexts ``to \ldots with'' and ``not \ldots with''
-    \item neighbouring words of these contexts share the same PoS
-\end{itemize}
-}
-
-\end{frame}
-
-\begin{frame}[t]{More Formally}
-
-Construct a bipartite graph
-\begin{itemize}
-    \item Nodes on the top layer denote word types (bilingual phrase pairs)
-    \item Nodes on the bottom layer denote context types (monlingual/bilingual words)
-    \item Edges connect words and their contexts
-\end{itemize}
-
-\includegraphics[width=\columnwidth]{bipartite.pdf}
-
-\end{frame}
-
-\begin{frame}[t]{Clustering}
-
-Task is to cluster the graph into sub-graphs. Nodes in the sub-graphs should be
-\begin{itemize}
-\item strongly connected to one another
-\item weakly connected to nodes outside the sub-graph
-\item could formulate as either \emph{hard} or \emph{soft} clustering
-\end{itemize}
-Choose \alert{soft clustering} to allow for syntactic and semantic ambiguity
-
-\centering
-\includegraphics[width=0.7\columnwidth]{bipartite_lda.pdf}
-
-\end{frame}
-
-\begin{frame}[t]{Latent Dirichlet Allocation (LDA)}
-
-LDA is a generative model which treats documents as bags of words
-\begin{itemize}
-    \item each word is assign a \alert{topic} (cluster tag)
-    \item words are generated from a topic-specific multinomial
-    \item topics are \alert{tied} across a document using a Dirichlet prior
-    \item $\alpha < 1$ biases towards \alert{sparse} distributions, i.e., topic reuse
-    \item inferred $\theta_d$ describes a document and $\phi_t$ describes a topic
-\end{itemize}
-
-\vspace{-3ex}
-\includegraphics[scale=0.55]{lda.pdf}
-
-\end{frame}
-
-\begin{frame}[t]{LDA over Contexts}
-
-Generative story:
-\begin{itemize}
-    \item for each word type $w$
-    \item for each of the $L$ contexts
-    \item first we draw a topic $t$, then generate the context $\vec{c}$ given the topic
-    \item the Dirichlet prior ties the topics for each $w$
-    \item we're primarily interested in the learnt $\theta$ values
-\end{itemize}
-
-\includegraphics[scale=0.4]{context_lda.pdf}
-
-\end{frame}
-
-\end{document}