\documentclass[12pt,a4paper]{article}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
%%%%%%%%%%%%%%%%%%%%%%
%%% PACKAGES %%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%
\usepackage[margin=3.5cm]{geometry}
\usepackage{pgf-pie}
\usepackage{bchart}
\usepackage{booktabs}
\usepackage{forest}
\usepackage{enumitem}
\usepackage{colortbl}
\usepackage{fancyvrb}
\usepackage{times}
\usepackage{qtree}
\title{Report on query responses}
\author{Imię Nazwisko}
\date{\today}


\begin{document}
%\maketitle





%A corpus-based taxonomy of query responses was presented. Seven classes of query-responses were described. The classification is focused on the function  $q2$  (the question given as a response) serves in relation to $q1$ (the initial question).


We conducted our study using the BNC since it is a general corpus with a variety of domains and genres. However, we also wanted to check how the classes are distributed in more genre-specific corpora. To do this, we decided to study the following corpora:
\begin{enumerate}[label=(\roman*)]
	\item the Child Language Data Exchange System (CHILDES),   which contains adult-child conversations,
	\item the Basic Electricity and Electronics Corpus	(BEE), which contains tutorial dialogues from electronics courses,
	\item the  SRI/CMU American Express dialogues (AMEX), which contains conversations with travel agents. 
\end{enumerate}

\bigskip

Answers provided to query responses (BNC) in \% of the total per category are presented in the table below.

\begin{center}
\begin{tabular}{|>{\columncolor{gray}}lll}
Category & Ans. to $q2$ & Ans. to $q1$ \\ \midrule
	{\tt DP} & 76.85 & 62.96 \\ 
	{\tt MOTIV} & 78.05 & 51.22 \\ 
	{\tt NO ANSW} & 80.77 & 11.54 \\ 
	{\tt FORM} & 68.75 & 81.25 \\ 
	{\tt IND} & 53.85 & 100 \\ 
	{\tt IGNORE} & 50 & 16.67 \\ \bottomrule
\end{tabular}
\end{center}


\bigskip

Now we may present a summary of  the distributions  in each corpus (in \% of the total, without CRs).

\begin{bchart}[max=100, step=50, unit=\%]
\bcbar[label={\tt DP}, value=BNC: 49\%, color=blue]{49}
\bcbar[value=CHILDES: 25.58\%, color=blue!80]{25.58}
\bcbar[value=BEE: 80\%, color=blue!50]{80}
\bcbar[value=AMEX: 100\%, color=blue!10]{100}
\bcskip{5pt}%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bcbar[label={\tt IGNORE}, color=orange, value=BNC: 3\%]{3}
\bcbar[value=CHILDES: 39.53\%, color=orange!40]{39.53}
\bcbar[value=BEE: 2.86\%, color=orange!10]{2.86}
\bcxlabel{\% without CRs} %%%%%%%%%%%%%
\end{bchart}

\newpage

In what follows we will take a closer look on classes distribution in the BEE corpus. Let us remind that this corpus consists of task-oriented dialogues, thus it may be noticed, that DP is the largest class of q-responses observed here.

\begin{center}
\begin{tikzpicture}
	\pie[explode={0, 0.3, 0, 0}, text=legend]{62/ Dependent q-responses , 22/ Clarification requests , 14/ No answer q-responses , 2/ Ignoring q-responses}
\end{tikzpicture}
\end{center}


\end{document}


As for the annotation reliability, the results are following. The Fleiss K  for all five annotators was $0.64$ (i.e. substantial) with 51\% agreement  over 100 cases. The agreements between the main annotator and others  were all substantial:
\begin{enumerate}[label=\emph{\alph*)}]
	\item main and second annotator: $K=0.67$ with 73\% agreement;
	\item main and third annotator: $K=0.65$ with 72\% agreement;
	\item main and fourth annotator: $K=0.61$ with 69\% agreement;
	\item main and fifth annotator: $K=0.63$ with 70\% agreement.
\end{enumerate}





The reported K values were established with \textsf{R} environment with the following code:

\begin{Verbatim}[frame=single, numbers=left]
inter <- read.csv("./inter.csv", sep=";")
> library(irr)
Ladowanie wymaganego pakietu: lpSolve
> kappam.fleiss(inter)
Fleiss Kappa for m Raters

Subjects = 100
Raters = 3
Kappa = 0.797

z = 44.6
p-value = 0
\end{Verbatim}

\newpage

The reported results may be useful for the analysis with the so called erotetic search scenarios. The idea of e-scenario is presented in the diagram below.

\begin{center}
\begin{forest}
	[\textbf{initial question (problem)} [premises [\textit{query1}? [Y, circle, draw [\textit{query2}? [Y] [N, circle, draw [\textbf{solution}]] ]] [N]]]]
\end{forest}
\end{center}

\bigskip

We may now present an example of a dialogue with such a structure retrieved from the aforementioned BEE corpus.

\begin{center}
\Tree[
.{Is it the case that there is a zero voltage reading?\\\textit{When there is a non-zero voltage reading, there is a difference in
		charge.}\\\textit{If there is a zero voltage reading, there is no difference in
		charge.}\\\textit{There is a difference in charge if and only if the voltage is ``created'' or ``used up''.}\\In a switch, is there any voltage being ``created'' or ``used up''?}
{Yes\\There is a difference in charge.\\There is a non-zero voltage reading.} 
{No\\There is no difference in charge\\There is a zero voltage reading.}
]
\end{center}
\end{document}