Commit bd0b948e authored by Marc Feger's avatar Marc Feger
Browse files

Refactor Methods

parent 8b8d9709
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......@@ -4,64 +4,50 @@
\begin{frame}
\frametitle{PageRank}
\begin{columns}
\column{0.6\textwidth}
\column{0.5\textwidth}
\begin{itemize}
\item PageRank by \cite{PageRank1999} was originally used to evaluate relevant websites via their links
\item Websites can be replaced by arguments
\item Linking results due to the reuse of conclusions and premises
\item In this work it was used:
\begin{itemize}
\item Custom-made PageRank
\item NetworkX PageRank
\item NetworkX-Scipy PageRank
\end{itemize}
\item PageRank originally used for websites
\item Websites replaced by arguments
\end{itemize}
\begin{block}{Custom-made PageRank}
\begin{equation*}
p_t(c_i) = \left\{
\begin{array}{lr}
(1 - \alpha)\frac{1}{|D|} + \alpha \sum_{j}{\frac{p_{t-1}(c_j)}{|P'_j|}} & : t > 0\\
\frac{1}{|D|} & : t = 0
(1 - \alpha)G_{rel} + \alpha L_{rel} & : t > 0\\
G_{rel} & : t = 0
\end{array}
\right.
\end{equation*}
\end{block}
\column{0.4\textwidth}
\column{0.5\textwidth}
\includegraphics[scale=0.25]{bilder/ExampleGraph.png}
\end{columns}
\end{frame}
\begin{frame}
\frametitle{WordNet}
\begin{itemize}
\item As knowledge based method $Sim(T_1, T_2)$ of \cite{Mihalcea2006similarity} was used
\item $Sim(T_1, T_2)$ determines the semantic similarity of $T_1,T_2$ by mutually picking up highly similar concepts
\item The concepts were determined via WordNet
\item For $maxSim$ the $CoSim$ of \cite{Wu1994distance} was used
\end{itemize}
\begin{block}{$Sim(T_1, T_2)$}
\begin{equation*}
\frac{1}{2}(\frac{\sum_{w \in {T_1}}{maxSim(w, T_2)\cdot idf(w)}}{\sum_{w \in {T_1}}{idf(w)}}+\frac{\sum_{w \in {T_2}}{maxSim(w, T_1)\cdot idf(w)}}{\sum_{w \in {T_2}}{idf(w)}})
\end{equation*}
\end{block}
\begin{itemize}
\item Analogously, the average conceptual similarity between $T_1$ and $T_2$ was used as a weakened variant
\end{itemize}
\begin{columns}
\column{0.5\textwidth}
\begin{itemize}
\item Knowledge-based method
\item Conceptual similarity between conclusion and premise
\end{itemize}
\column{0.5\textwidth}
\includegraphics[scale=0.33]{bilder/Wordnet.png}
\end{columns}
\end{frame}
\begin{frame}
\frametitle{Similarity}
\begin{itemize}
\item For ranking the arguments, we measured the semantic similarity
between the premises and conclusions
\item Argument were embedded word-wise in an averaged vector space
\item The resulting similarity was calculated by using $Cos(c, p)$
\end{itemize}
\begin{block}{Embeddings used}
BERT by \cite{devlin2018bert}
ELMo by \cite{Peters:2018ELMo}
GloVe by \cite{pennington2014glove}
\end{block}
\begin{columns}
\column{0.5\textwidth}
\begin{itemize}
\item Semantic similarity
\item Different embeddings
\item BERT, ELMo and GloVe
\item Similarity over $Cos(C, P)$
\end{itemize}
\column{0.5\textwidth}
\includegraphics[scale=0.4]{bilder/Vectors.png}
\end{columns}
\end{frame}
\begin{frame}
\frametitle{Sentiment}
......
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