2014_acl_reganchor.tex

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\title{Anchors Regularized: Adding Robustness and Extensibility \\
to Scalable Topic-Modeling Algorithms}

\author{Thang Nguyen  \\
  iSchool and \abr{umiacs}, \\
  University of Maryland \\
  and National Library of Medicine, \\
  National Institutes of Health \\
  \email{daithang@umiacs.umd.edu} \\\And
  Yuening Hu \\
  Computer Science \\
  University of Maryland \\
  \email{ynhu@cs.umd.edu} \\ \And
  Jordan Boyd-Graber \\
  iSchool and \abr{umiacs} \\
  University of Maryland \\
  \email{jbg@umiacs.umd.edu} \\
}

\date{}



\begin{document}

%\maketitle

% TODO
% 1.  Explain different corpora for TI
% 2.  Hyperparameter selection for HL
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% 4.  Explain why NIPS has poor WIKITI
% 5.  Remove informed prior equation
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\begin{abstract}
  Spectral methods offer scalable alternatives to Markov chain Monte
  Carlo and expectation maximization.  However, these new methods lack
  the rich priors associated with probabilistic models.  We examine
  Arora et al.'s anchor words algorithm for topic modeling and develop
  new, regularized algorithms that not only mathematically resemble
  Gaussian and Dirichlet priors but also improve the interpretability
  of topic models.  Our new regularization approaches make these
  efficient algorithms more flexible; we also show that these methods can
  be combined with informed priors.
\end{abstract}

\input{2014_acl_reganchor/sections/intro}
\input{2014_acl_reganchor/sections/background}
\input{2014_acl_reganchor/sections/model}
\input{2014_acl_reganchor/sections/experiments}
\input{2014_acl_reganchor/sections/discussion}
\input{2014_acl_reganchor/sections/conclusion}

\section*{Acknowledgments}

We would like to thank the anonymous reviewers, Hal Daum\'e III, Ke Wu,
and Ke Zhai for their helpful comments.  This work was supported by
\abr{nsf} Grant IIS-1320538.  Boyd-Graber is also supported by
\abr{nsf} Grant CCF-1018625.  Any opinions, findings, conclusions, or
recommendations expressed here are those of the authors and do not
necessarily reflect the view of the sponsor.

\newpage

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