Add new presentation with vocabulary extraction

bilder/Pipeline_Vocab.svg

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" 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\ No newline at end of file

master.tex

@@ -187,113 +187,54 @@ Department for Computer-Networks and Communication-Systems}
 	\end{itemize}
 \end{frame}
 
-\begin{frame}
-	\begin{center}
-		\includegraphics[width=0.8\textwidth]{bilder/Overview.png}
-
-		Overview by \cite{SchaeferStede2021}.
-		CA = Corpus Annotation,
-		AD = Argument Detection,
-		SD = Stance Detection,
-		CD = Claim Detection.
-	\end{center}
-\end{frame}
 
 \begin{frame}
 	\begin{center}
-		\includegraphics[width=0.75\textwidth]{bilder/AnnotationSchema.png}
-
-		Overview by \cite{SchaeferStede2021}.
-	\end{center}
-\end{frame}
-
-\begin{frame}
-	\begin{center}
-		\begin{columns}
-			\column{0.5\textwidth}
-				\begin{center}
-					\includegraphics[width=0.8\textwidth]{bilder/Corpus.png}
-	
-					Corpora + IAA reported \cite{SchaeferStede2021}.
-					ADU = Argument Discourse Unit,
-					cx = Cohens Kappa
-				\end{center}
-			\column{0.5\textwidth}
-				\begin{center}
-					\includegraphics[width=0.8\textwidth]{bilder/PrevResults.png}
-		
-					Results reported by \cite{SchaeferStede2021}.
-					ADU = Argument Discourse Unit,
-					cx = Cohens Kappa
-				\end{center}
-			\end{columns}
+		\includegraphics[scale=0.3]{bilder/Pipeline}
 	\end{center}
-
 \end{frame}
 
-
 \section{Current}
 \begin{frame}
 	\begin{center}
-		\includegraphics[scale=0.3]{bilder/Pipeline}
+		\includegraphics[scale=0.3]{bilder/Pipeline_Vocab}
 	\end{center}
 \end{frame}
 
-\begin{frame}{\#abortion}
+\begin{frame}{Kialo}
 	\begin{center}
 		\begin{columns}
 			\column{0.5\textwidth}
-				\begin{center}
-					\includegraphics[scale=0.2]{bilder/Abortion_WordCloud.png}
-				\end{center}
+				\includegraphics[scale=0.1]{bilder/kialo.jpg}
 			\column{0.5\textwidth}
-				\begin{center}
 				\begin{itemize}
-						\item Topic: Abortion
-						\item Time: 2021-08-15 - 2021-10-15
-						\item Root-Tweets: 15.273
-						\begin{itemize}
-							\item Conversation-Starter: 2.250
-						\end{itemize}
-						\item Conversation-Tweets: 14.569
+					\item Labeled Arguments (Pro/Con)
+					\item Strict Guidelines
+					\item Reviewed
 				\end{itemize}
-				\end{center}
 		\end{columns}
 	\end{center}
+	\begin{tcolorbox}[colback=cyan!5!white,colframe=cyan!75!black,title=Kialo Claim]
+		A \textbf{claim} is, in its most basic form, a \textbf{single piece} of an \textbf{argument} made in a discussion. 
+		[...]
+		This \textbf{allows} you to \textbf{inspect} each step of the \textbf{thinking behind} an \textbf{argument}. 
+	\end{tcolorbox}
 \end{frame}
 
-\begin{frame}{\#abortion}
-	\begin{center}
-		\includegraphics[scale=0.3]{bilder/Abortion_Time.png}
-	\end{center}
-\end{frame}
-
-\begin{frame}{\#brexit}
-	\begin{center}
-		\begin{columns}
-			\column{0.5\textwidth}
-				\begin{center}
-					\includegraphics[scale=0.2]{bilder/Brexit_WordCloud.png}
-				\end{center}
-			\column{0.5\textwidth}
+\begin{frame}{Datasets}
 	\begin{center}
 		\begin{itemize}
-						\item Topic: Brexit
-						\item Time: 2020-01-01 - 2020-03-01
-						\item Root-Tweets: 151.090
+			\item [Kialo]:
 			\begin{itemize}
-							\item Conversation-Starter: 24.806
+				\item[abortion]: 3.201 Claims
+				\item[brexit]: 2.554  Claims
+			\end{itemize}
+			\item [Twitter]:
+			\begin{itemize}
+				\item[abortion]: 2.250 Root-Tweets
+				\item[brexit]: 24.806 Root-Tweets
 			\end{itemize} 
-						\item Conversation-Tweets: 275.968
 		\end{itemize}
-				\end{center}
-			\end{columns}
-	\end{center}
-\end{frame}
-
-\begin{frame}{\#brexit}
-	\begin{center}
-		\includegraphics[scale=0.3]{bilder/Brexit_Time.png}
 	\end{center}
 \end{frame}
 
@@ -307,9 +248,9 @@ Department for Computer-Networks and Communication-Systems}
 						\begin{tabular}{@{}lll@{}}
 							\toprule
 							\textbf{Open} & \textbf{Closed} & \textbf{Other} \\ \midrule
-								ADJ & \textbf{AUX} & PUNCT \\
-								ADV & \textbf{CCONJ} & SYM \\
-								INTJ & DET & X \\
+								\textbf{ADJ} & \textbf{AUX} & PUNCT \\
+								\textbf{ADV} & \textbf{CCONJ} & SYM \\
+								INTJ & \textbf{DET} & X \\
 								NOUN & NUM &  \\
 								PROPN & PART &  \\
 								\textbf{VERB} & PRON &  \\
@@ -320,138 +261,141 @@ Department for Computer-Networks and Communication-Systems}
 				\end{center}
 			\column{0.6\textwidth}
 				\begin{itemize}
-					\item Universal POS Tagging
-					\item spaCy: en\_core\_web\_sm
-					\item Open v. Closed $\approx$ Content v. Function
-					\item Selection supposed by \cite{Knott1993}.
+					\item \textbf{ADJ}: \\
+						modify, specify nouns
+					\item \textbf{ADV}: \\
+						modify, specify verbs	
+					\item \textbf{*VERB}: \\
+						typically signal events and actions
+					\item \textbf{*AUX}: \\
+						adds funct. or gramma. meaning
+					\item \textbf{*CCONJ}: \\
+						expresses a semantic relationship
+					\item \textbf{DET}: \\
+						express references in context
+					\item \textbf{*SCONJ}:\\
+						making one construction part of the other
 				\end{itemize}
 			\end{columns}
 	\end{center}
 \end{frame}
 
-\begin{frame}{POS-Tagging}
+\begin{frame}{POS-Tag Distribution by words}
 	\begin{center}
-		\begin{columns}
-			\column{0.4\textwidth}
-				\begin{center}
-					\begin{table}[]
-						\resizebox{0.8\textwidth}{!}{%
-						\begin{tabular}{@{}lll@{}}
-							\toprule
-							\textbf{Open} & \textbf{Closed} & \textbf{Other} \\ \midrule
-								ADJ & \textbf{AUX} & PUNCT \\
-								ADV & \textbf{CCONJ} & SYM \\
-								INTJ & DET & X \\
-								NOUN & NUM &  \\
-								PROPN & PART &  \\
-								\textbf{VERB} & PRON &  \\
-						 		& \textbf{SCONJ} &  \\ \bottomrule
-							\end{tabular}%
-						}
-						\end{table}
+		\includegraphics[width=\textwidth]{bilder/PosDis.png}
 	\end{center}
-			\column{0.6\textwidth}
+\end{frame}
+
+\begin{frame}{Vocabulary Selection}
 	\begin{itemize}
-					\item \textbf{VERB}: \\
-						typically signal events and actions
-					\item \textbf{AUX}: \\
-						adds funct. or gramma. meaning
-					\item \textbf{CCONJ}: \\ 
-						links words or larger constituents,\\
-						expresses a semantic relationship
-					\item \textbf{SCONJ}:\\
-						making one construction part of the other,\\
-						join independent and a dependent clause
+		\item [1] Sorting Word-Lemma per POS-Tag by occurance
+		\item [2] Select Index with best Spearman-Rank-Correlation
+		\item [3] Intersection of all Dataset per POS-Tag till best Index
 	\end{itemize}
-			\end{columns}
-	\end{center}
 \end{frame}
 
-\begin{frame}{POS-Tagging Conversation Starter}
+\begin{frame}{Step 1: Example SCONJ}
 	\begin{center}
-		\includegraphics[scale=0.3]{bilder/Abortion_POS.png}
+	\begin{tabular}{lllll}
+			\toprule
+				{} & \textbf{abortion\_kialo} & \textbf{brexit\_kialo} & \textbf{abortion} &   \textbf{brexit} \\
+			\midrule
+				0  &           that &         that &     that &     that \\
+				1  &             if &           if &       if &       if \\
+				2  & \textbf{because} &           \textbf{as} &      how &      how \\
+				3  &           when &      \textbf{because} &     when &       \textbf{as} \\
+				4  &             \textbf{as} &         when &      why &     when \\
+				5  &          where &        since &  \textbf{because} &      why \\
+				6  &        whether &          for &       \textbf{as} &  \textbf{because} \\
+				7  &            for &          how &    where &    where \\
+				8  &          while &        while &    while &      for \\
+				9  &          since &         than &    since &    since \\
+				10 &            how &        where &      for &    after \\
+				11 &            why &      whether &    until &    while \\
+				12 &           than &          why &   before &       so \\
+				13 &           upon &       before &    after &     like \\
+				14 &          after &        after &  whether &     than \\
+			\bottomrule
+		\end{tabular}
 	\end{center}
 \end{frame}
 
-\begin{frame}{POS-Tagging Conversation Starter}
+\begin{frame}{Step 2: Best Indices}
 	\begin{center}
-		\includegraphics[scale=0.3]{bilder/Brexit_POS.png}
+		\begin{tabular}{lll}
+			\toprule
+				\textbf{Tag} & \textbf{Spearman} & \textbf{Index} \\
+			\midrule
+				SCONJ & 0.93 & 26 \\
+				CCONJ & 0.83 & 10 \\
+				AUX & 0.91 & 26 \\
+				VERB & 1.0 & 10 \\
+				DET & 0.97 & 27 \\
+				ADJ & 1.0 & 10 \\
+				ADV & 0.89 & 30 \\
+			\bottomrule
+		\end{tabular}
 	\end{center}
 \end{frame}
 
-\begin{frame}{POS-Tagging Words (PWords)}
-	\begin{table}[]
-		\centering
-		\resizebox{\textwidth}{!}{%
-			\begin{tabular}{@{}llllllll@{}}
+\begin{frame}{Step 3: Vocabulary Selection}
+	\begin{center}
+	\resizebox{0.75\textwidth}{!}{
+		\begin{tabular}{llllllll}
 			\toprule
-				\multicolumn{2}{c}{\textbf{SCONJ}} & \multicolumn{2}{c}{\textbf{CONJ}} & \multicolumn{2}{c}{\textbf{AUX}} & \multicolumn{2}{c}{\textbf{VERB}} \\ \midrule
-				\textit{\textbf{Brexit}} & \multicolumn{1}{l|}{\textit{\textbf{Abortion}}} & \textit{\textbf{Brexit}} & \multicolumn{1}{l|}{\textit{\textbf{Abortion}}} & \textit{\textbf{Brexit}} & \multicolumn{1}{l|}{\textit{\textbf{Abortion}}} & \textit{\textbf{Brexit}} & \textit{\textbf{Abortion}} \\ \midrule
-				that & \multicolumn{1}{l|}{that} & and & \multicolumn{1}{l|}{and} & is & \multicolumn{1}{l|}{is} & have & have \\
-				if & \multicolumn{1}{l|}{if} & but & \multicolumn{1}{l|}{but} & will & \multicolumn{1}{l|}{are} & get & get \\
-				how & \multicolumn{1}{l|}{how} & or & \multicolumn{1}{l|}{or} & be & \multicolumn{1}{l|}{be} & going & do \\
-				as & \multicolumn{1}{l|}{when} & + & \multicolumn{1}{l|}{+} & are & \multicolumn{1}{l|}{will} & know & want \\
-				when & \multicolumn{1}{l|}{why} & plus & \multicolumn{1}{l|}{yet} & have & \multicolumn{1}{l|}{can} & \textbf{leave} & know \\
-				why & \multicolumn{1}{l|}{because} & so & \multicolumn{1}{l|}{nor} & can & \multicolumn{1}{l|}{do} & \textbf{voted} & make \\
-				because & \multicolumn{1}{l|}{as} & both & \multicolumn{1}{l|}{both} & has & \multicolumn{1}{l|}{should} & want & \textbf{need} \\
-				where & \multicolumn{1}{l|}{where} & yet & \multicolumn{1}{l|}{neither} & was & \multicolumn{1}{l|}{was} & \textbf{see} & think \\
-				since & \multicolumn{1}{l|}{while} & either & \multicolumn{1}{l|}{so} & do & \multicolumn{1}{l|}{have} & think & let \\
-				for & \multicolumn{1}{l|}{since} & nor & \multicolumn{1}{l|}{either} & am & \multicolumn{1}{l|}{would} & let & \textbf{has} \\
-				after & \multicolumn{1}{l|}{after} & n & \multicolumn{1}{l|}{n} & would & \multicolumn{1}{l|}{has} & \textbf{done} & \textbf{say} \\
-				while & \multicolumn{1}{l|}{\textbf{until}} & • & \multicolumn{1}{l|}{plus} & \textbf{been} & \multicolumn{1}{l|}{am} & do & going \\
-				\textbf{so} & \multicolumn{1}{l|}{for} & neither & \multicolumn{1}{l|}{•} & were & \multicolumn{1}{l|}{\textbf{does}} & \textbf{go} & \textbf{had} \\
-				\textbf{like} & \multicolumn{1}{l|}{\textbf{before}} & \textbf{minus} & \multicolumn{1}{l|}{} & \textbf{could} & \multicolumn{1}{l|}{\textbf{being}} & \textbf{leaving} & \textbf{support} \\
-				\textbf{despite} & \multicolumn{1}{l|}{\textbf{whether}} & \textbf{daythe} & \multicolumn{1}{l|}{} & should & \multicolumn{1}{l|}{were} & make & \textbf{said} \\ \bottomrule
-				\end{tabular}%
+				{} &    \textbf{SCONJ} &   \textbf{CCONJ} &     \textbf{AUX} &  \textbf{*VERB} &       \textbf{DET} &    \textbf{*ADJ} &     \textbf{*ADV} \\
+			\midrule
+				0  &    after &     and &      \textbf{be} &    \textbf{be} &         a &   more &    also \\
+				1  &       as &    both &     can &    \textbf{do} &       all &  other &  always \\
+				2  &  because &     but &   could &  \textbf{have} &        an &        &      as \\
+				3  &   before &  either &      \textbf{do} &  make &   another &        &    even \\
+				4  &  despite &     nor &     get &       &       any &        &    just \\
+				5  &      for &      or &    \textbf{have} &       &      both &        &    long \\
+				6  &      how &      so &  having &       &      each &        &    more \\
+				7  &       if &     yet &     may &       &    either &        &    most \\
+				8  &     once &         &   might &       &     every &        &   never \\
+				9  &    since &         &    must &       &      half &        &    only \\
+				10 &       so &         &    need &       &        no &        &      so \\
+				11 &     than &         &   shall &       &     quite &        &   still \\
+				12 &     that &         &  should &       &      some &        &    then \\
+				13 &   though &         &      to &       &      such &        &     too \\
+				14 &   unless &         &    will &       &      that &        &    very \\
+				15 &    until &         &   would &       &       the &        &    well \\
+				16 &     upon &         &         &       &     these &        &         \\
+				17 &     when &         &         &       &      this &        &         \\
+				18 &    where &         &         &       &     those &        &         \\
+				19 &  whether &         &         &       &      what &        &         \\
+				20 &    while &         &         &       &  whatever &        &         \\
+				21 &      why &         &         &       &     which &        &         \\
+				22 &     with &         &         &       &     whose &        &         \\
+			\bottomrule
+		\end{tabular}
 	}
-	\end{table}
+	\end{center}
 \end{frame}
 
-\begin{frame}{Pre-Selection}
+\begin{frame}{First Impression}
+	Using vocab. with SCONJ, CCONJ, AUX, DET:
 	\begin{itemize}
-		\item Conditions:
-		\begin{itemize}
-			\item [1] At least one word in PWords
-			\item [2] At least 200 characters long
-		\end{itemize}
-		\item \#Abortion: $\Rightarrow$ 1.400 Candidates
-		\item \#Brexit: $\Rightarrow$ 11.842 Candidates
+		\item [$\Rightarrow$] 0.069\% dropoff in abortion
+		\item [$\Rightarrow$] 0.066\% dropoff in brexit
+		\item [$\Rightarrow$] Dropoff = Adds, Allegations, Comments, Links etc.
+		\item [$\Rightarrow$] Root-Tweets $\geq$200 chars) could be better
 	\end{itemize}
 \end{frame}
 
-\begin{frame}
-	\begin{tcolorbox}[colback=cyan!5!white,colframe=cyan!75!black,title=Example 1: \#Brexit]
-		\textbf{How} ironic \textbf{is} this? \textbf{For} 4 years, \textcolor{orange}{\#Remainers} predicted \textbf{that} \textcolor{orange}{\#Brexit} \textbf{would} cause a catastrophic national crisis. Now \textbf{that} Brexit \textbf{has} happened at long last, we \textbf{are} indeed facing a catastrophic national crisis, \textbf{but} it \textbf{has} absolutely nothing to \textbf{do} with Brexit. Go figure \textbf{that} one out.
-		%\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}
-	\begin{tcolorbox}[colback=cyan!5!white,colframe=cyan!75!black,title=Example 2: \#Brexit]
-		Labour cannot win the next general election with \textcolor{magenta}{@Keir\_Starmer} \textbf{because} voters \textbf{do}n’t trust him. Starmer \textbf{is} a proven liar \textbf{and} fraud who tried to cheat 17.4m voters out of their \textcolor{orange}{\#Brexit} referendum victory. Starmer \textbf{is} unelectable. \textcolor{orange}{\#LabourDebate} \textcolor{orange}{\#labourhustings}
-		%\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}
-	\begin{tcolorbox}[colback=cyan!5!white,colframe=cyan!75!black,title=Example 3: \#Brexit]
-		\textcolor{orange}{\#SaturdayThought} \textbf{If} \textcolor{orange}{\#PMs} "oven-ready Brexit deal" \textbf{is so} "oven-ready," \textbf{why has} he not got full agreement of the EU \textbf{yet}? \textbf{After} all, \textcolor{magenta}{@Conservatives} keep telling us \textbf{that} \textcolor{orange}{\#Brexit} \textbf{is} over now.\textcolor{orange}{\#JustAsking}
-		%\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}
-	\begin{tcolorbox}[colback=cyan!5!white,colframe=cyan!75!black,title=Example 4: \#Abortion]
-		POV: Stop saying NOBODY \textbf{is} “pro-abortion."  It\textbf{'s} \textcolor{orange}{\#stigmatising}. \textbf{Its} simply inaccurate, Plenty of folk \textbf{are} unapologetically \textcolor{orange}{\#proabortion}; \textbf{because} there\textbf{'s} nothing wrong with \textcolor{orange}{\#Abortion} -it\textbf{`s} a necessary health care procedure \textbf{that} saves lives.
-		%\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}
-
-
 \section{Next}
 \begin{frame}
 	\begin{itemize}
-		\item [1] More controverse topics
-		\item [2] Cleaning of PWords
-		\item [3] PWords and POS-Distribution for Kialo
-		\item [4] Annotation / Quality-Check on Candidate-Subset
+		\item [1] Selection on SCONJ, CCONJ, AUX
+		\item [2] Selection sampling:
+		\begin{itemize}
+			\item [] \textbf{Sample 1}: Dropoff
+			\item [] \textbf{Sample 2}: Root-Tweets with length $<$ 200
+			\item [] \textbf{Sample 3}: Root-Tweets with length $\geq$200
+		\end{itemize}
+		\item [3] Annotation of samples
+		\item [4] Fine-Tuning of argumentive vocabulary
 	\end{itemize}
 \end{frame}