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main.tex
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%
% File naacl2019.tex
%
%% Based on the style files for ACL 2018 and NAACL 2018, which were
%% Based on the style files for ACL-2015, with some improvements
%% taken from the NAACL-2016 style
%% Based on the style files for ACL-2014, which were, in turn,
%% based on ACL-2013, ACL-2012, ACL-2011, ACL-2010, ACL-IJCNLP-2009,
%% EACL-2009, IJCNLP-2008...
%% Based on the style files for EACL 2006 by
%%e.agirre@ehu.es or Sergi.Balari@uab.es
%% and that of ACL 08 by Joakim Nivre and Noah Smith
\documentclass[11pt,a4paper]{article}
\usepackage[hyperref]{naaclhlt2019}
\usepackage{times}
\usepackage{latexsym}
\usepackage{tabularx}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{latexsym}
\usepackage{enumitem}
\usepackage{booktabs}
\usepackage{url}
\usepackage{color}
\usepackage{verbatim}
\usepackage{calc}
\usepackage{arydshln}
\setlength\dashlinedash{0.7pt}
\setlength\dashlinegap{1.pt}
\setlength\arrayrulewidth{0.3pt}
\usepackage{mathtools}
\usepackage[draft,textsize=tiny]{todonotes}
\newcommand{\raq}[1]{\textcolor{blue}{R: #1}}
\newcommand{\marco}[1]{\textcolor{red}{M: #1}}
\newcommand{\gbt}[1]{\textcolor{green}{G: #1}}
\newcommand{\new}[1]{\textcolor{red}{#1}}
\newcommand{\redd}{Reddit$_{13}$}
\usepackage{soul}
\aclfinalcopy % Uncomment this line for the final submission
%\def\aclpaperid{***} % Enter the acl Paper ID here
%\setlength\titlebox{5cm}
% You can expand the titlebox if you need extra space
% to show all the authors. Please do not make the titlebox
% smaller than 5cm (the original size); we will check this
% in the camera-ready version and ask you to change it back.
\newcommand\BibTeX{B{\sc ib}\TeX}
\title{Short-Term Meaning Shift: A Distributional Exploration}
\author{Marco Del Tredici$^*$ \ \ Raquel Fern\'andez$^*$ \ \ Gemma Boleda$^\dagger$\\
$^*$University of Amsterdam \ \ \ \ $^\dagger$Universitat Pompeu Fabra\\
{\tt \{m.deltredici|raquel.fernandez\}@uva.nl}\\
{\tt gemma.boleda@upf.edu}
}
\date{}
\begin{document}
\maketitle
\begin{abstract}
We present the first exploration of meaning shift over short periods of time in online communities using distributional representations. We create a small annotated dataset and use it to assess the performance of a standard model for meaning shift detection on short-term meaning shift. We find that the model has problems distinguishing meaning shift from referential phenomena, and propose a measure of contextual variability to remedy this.
\end{abstract}
%============================
\section{Introduction}
\label{sect:Introduction}
\input{sec_introduction}
%============================
\section{Related Work}
\label{sect:Related_Work}
\input{sec_related_work}
%============================
\section{Experimental Setup}
\label{sec:setup}
\input{sec_setup}
%============================
\section{Types of Meaning Shift}
\label{sec:types}
\input{sec_linguistic_analysis}
%============================
\section{Modeling Results and Analysis}
\label{sec:results}
\input{sec_results}
%============================
\section{Conclusion}
\label{sect:conc}
\input{sec_conclusion}
\section*{Acknowledgements}
The research carried out by the Amsterdam section of the team was partially funded by the Netherlands Organisation for Scientific Research (NWO) under VIDI grant no.~276-89-008, {\em Asymmetry in Conversation}.
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 715154), and from the Spanish Ram\'on y Cajal programme (grant RYC-2015-18907). This paper reflects the authors' view only, and the EU is not responsible for any use that may be made of the information it contains.
\begin{flushright}
\includegraphics[width=0.8cm]{flag_yellow_low.jpeg}
\includegraphics[width=0.8cm]{LOGO-ERC.jpg}
\end{flushright}
\bibliography{naaclhlt2019}
\bibliographystyle{acl_natbib}
\appendix
%\section{Further details on Data and Model}
%\label{sec:further_details_data_model}
%
%We downloaded Reddit data using the Python package Praw: \url{https://pypi.python.org/pypi/praw/}.
%
%The model was implemented using the Python package Gensim: \url{https://pypi.python.org/pypi/gensim/}.
\section{Further Details on Evaluation Dataset}
\label{sec:further_details_data_model}
For our experiment, we considered content words only, which we identified by using the external list of common words available at \url{https://www.wordfrequency.info/free.asp}.
Three words were discarded from the initial list after analysis of the redditor data: `discord' and `owls' due to the homonymy with proper names not detected during survey's implementation; `tracking' because the chosen examples clearly mislead the judgements of the redditors.
As detailed in Section \ref{subsec:Evaluation dataset}, 26 members of r/LiverpoolFC participated in the survey, and each word received on average 8.8 judgements. We computed inter-annotator agreement as Krippendorff's alpha, and obtained $\alpha$ = 0.58, a relatively low value but common in semantic tasks \cite{artstein2008inter}.
The results of the annotation validate our initial word sampling procedure:
\begin{itemize}
\item the words that present a significant increase in frequency and were annotated as
meaning shift by us received an average shift annotation of 0.72 ($\pm$ 0.15);
\item the words that present a
significant increase in frequency but that were \emph{not} annotated as
meaning shift by us received an average shift annotation of 0.15 ($\pm$ 0.16);
\item the words that keep a constant frequency between $t_1$ and $t_2$, and we don't consider examples of meaning shift, got 0.07 ($\pm$ 0.12).
\end{itemize}
\end{document}
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