Add first slides about motivation, targets and the data

master.tex

@@ -171,6 +171,66 @@ Department for Computer-Networks and Communication-Systems}
 
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+\section{Motivation}
+\begin{frame}{Motivation}
+	\begin{itemize}
+		\item Online \textbf{Argumentation} daily on Twitter
+		\begin{itemize}
+			\item \textbf{Argumentation}: Usage of \textbf{Arguments}
+			\item \textbf{Arguments}: Motivated conclusions
+		\end{itemize}
+		\item Tweets vary strongly
+		\item How to find \textbf{argumentative} Tweets
+		\begin{itemize}
+			\item \textbf{Argumentive}: At least one \textbf{Argument} included
+		\end{itemize}
+	\end{itemize}
+\end{frame}
+
+\begin{frame}{Related Work}
+	\begin{itemize}
+		\item 10 paper according to \cite{schaefer2021}
+		\item Hardly any public data
+		\item Mostly \emph{SVM}, \emph{NB}, \emph{LR}, \emph{RF}, hardly any \emph{Transformer}
+	\end{itemize}
+\end{frame}
+
+\begin{frame}{Targets}
+	\begin{itemize}
+		\item [1] Collect and provide annotated Tweets in line with Twitter guidelines
+		\item [2] Fine-tune \emph{BERT} or \emph{BERTweet} to classify argumentative Tweets
+	\end{itemize}
+\end{frame}
+
+\section{Data}
+\begin{frame}{About the data}
+	\textbf{Argument}: $\langle c, P\rangle$
+
+	\textbf{Conclusion}: $c$
+	
+	\textbf{Premises}: $P = \{p_1, ..., p_n | n \geq 1\}$
+
+	\begin{tcolorbox}[colback=cyan!5!white,colframe=cyan!75!black,title=Example:]
+		\textcolor{red}{RT} \textcolor{magenta}{@SaysSheToday}: The $[$Dixie Chicks$]_{c}$ were attacked just for $[$using 1A right$]_{p_1}$ to say they were ashamed of GWB. They $[$didn’t commit treason$]_{c}$ $[$like the \textcolor{orange}{\#47Senators}$]_{p_2}$
+	\end{tcolorbox}
+\end{frame}
+
+\begin{frame}{About the data}
+	Pipeline, Reply-Trees, Abortion, Argument
+\end{frame}
+
+\begin{frame}
+	\begin{center}
+		\includegraphics[scale=0.05]{bilder/AbortionGraph}
+	\end{center}
+\end{frame}
+
+\begin{frame}
+	\begin{center}
+		\includegraphics[scale=0.2]{bilder/ReplyTree}
+	\end{center}
+\end{frame}
+
 \begin{frame}
 	\begin{center}
 		\begin{columns}

references.bib

+@article{schaefer2021,
+author = {Schaefer, Robin AND Stede, Manfred},
+title = {Argument Mining on Twitter: A survey},
+journal = {it - Information Technology},
+volume = {63},
+number = {1},
+year = {2021},
+pages = { 45-58 },
+doi = { 10.1515/itit-2020-0053 }
+} 
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