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2017_fntir_tm_applications.tex
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2017_fntir_tm_applications.tex
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%\documentclass[openany]{now} % creates the journal version
\documentclass{now} % creates the book pdf version
% the now document class sets various dimensions, so be sure to *not* set
% or alter dimensions in your latex code.
% be sure to remove all manual formatting commands such \newpage, \clearpage.
% hack
%\usepackage[UTF8]{ctex}
\usepackage{CJK}
\usepackage{mathtools}
\usepackage{comment}
\usepackage{makeidx}
\usepackage{bm}
\usepackage{mdwlist}
\usepackage[table]{xcolor}
\usepackage{algorithm}
\usepackage[noend]{algorithmic}
\usepackage{amsfonts}
\usepackage{xcolor}
\usepackage{multirow}
\usepackage{amssymb}
\usepackage{dsfont}
\usepackage{microtype}
\usepackage[all]{nowidow}
\usepackage{caption}
\usepackage{subcaption}
% a few definitions that are *not* needed in general:
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\newcommand{\disc}[1]{\mbox{Discrete}( #1)}
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\newcommand{\jbgcomment}[1]{ \colorbox{red}{ \parbox{.8\linewidth}{ JBG: #1} }}
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\else
\newcommand{\jbgcomment}[1]{ }
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\title{Applications of Topic Models}
\author{
Jordan Boyd-Graber \\
Department of Computer Science, \abr{umiacs}, Language Science \\
University of Maryland\footnote{Work completed while at University of Colorado} \\
\texttt{[email protected]}
\and
Yuening Hu \\
Google, Inc.\footnote{Work completed while at Yahoo!} \\
\texttt{[email protected]}
\and
David Mimno \\
Information Science \\
Cornell University \\
\texttt{[email protected]}
}
\makeindex
\begin{document}
\copyrightowner{J.~ Boyd-Graber, Y.~Hu and D.~Mimno}
\volume{11}
\issue{2-3}
\pubyear{2017}
\copyrightyear{2017}
\isbn{978-1-68083-308-9}
\doi{10.1561/1500000030}
\firstpage{143} % correct page number coming asap.
\lastpage{296}
\frontmatter % title page, contents, catalog information
\maketitle
\tableofcontents
\mainmatter
\begin{abstract}
\setcounter{page}{1}
How can a single person understand what's going on in a collection
of millions of documents? This is an increasingly common problem:
sifting through an organization's e-mails, understanding a decade
worth of newspapers, or characterizing a scientific field's
research. Topic models are a statistical framework that help users
understand large document collections: not just to find individual
documents but to understand the general themes
present in the collection.
This survey describes the recent academic and industrial
applications of topic models with the goal of launching a young
researcher capable of building their own applications of topic
models. In addition to topic models' effective application to
traditional problems like information retrieval, visualization,
statistical inference, multilingual modeling, and linguistic
understanding, this survey also reviews topic models' ability to
unlock large text collections for qualitative analysis. We review
their successful use by researchers to help understand fiction,
non-fiction, scientific publications, and political texts.
\end{abstract}
\input{chapters/introduction}
\input{chapters/ir}
\input{chapters/viz}
\input{chapters/nonfiction}
\input{chapters/science}
\input{chapters/fiction}
\input{chapters/css}
\input{chapters/multi_ling}
\input{chapters/building}
\input{chapters/conclusion}
\backmatter % references
\bibliographystyle{plainnat}
\bibliography{bib/journal-full,bib/clean,bib/alison}
\clearpage
\printindex
\end{document}