* Overview

You must critique an existing paper on Bayesian methods for text,
selected from an instructor-provided list. This will count for 20% of
your grade. Your critique should consist of a summary/explanation of
the key ideas, followed by detailed comments regarding the pros and
cons of the approach (with justifications) and
questions/comments/thoughts about the work, etc. You should
concentrate on the content (i.e., the problem, ideas, evaluation
methodology, impact, etc.). You should not comment on grammar or
typographical errors. Your critique should be longer (i.e., more
detailed) than a regular paper review, but should not be longer than 4
pages. There are instructions on how to write a review at
http://homes.cs.washington.edu/~mernst/advice/review-technical-paper.html
and there is an example regular paper review at
http://www.cs.uml.edu/~cchen/580-S06/sample_review.html. There is
excellent advice on reading papers at
http://www.cs.jhu.edu/~jason/advice/how-to-read-a-paper.html. Your
completed critique must be uploaded (in PDF format) to an
appropriately-named subdirectory (i.e., critique) of the cs691bm
directory in your home directory on EdLab by Fri. Dec. 7 at
11:59pm. Late critiques will not be accepted.

You must select a paper to critique from the list below. You MUST
confirm your selection with me (via email) by Weds. Oct. 31.


* Papers

  * Asuncion et al. "On Smoothing and Inference for Topic Models"
    http://www.ics.uci.edu/~asuncion/pubs/UAI_09.pdf

  * Chang et al. "Reading Tea Leaves: How Humans Interpret Topic
    Models" http://www.umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf

  * Chemudugunta et al. "Modeling General and Specific Aspects of
    Documents with a Probabilistic Topic Model"
    http://www.datalab.uci.edu/papers/special_words_NIPS06.pdf

  * Griffiths et al. "Integrating Topics and Syntax"
    http://psiexp.ss.uci.edu/research/papers/composite.pdf

  * Mackay "A Hierarchical Dirichlet Language Model"
    http://www.inference.phy.cam.ac.uk/mackay/lang4.pdf

  * Mimno and Blei "Bayesian Checking for Topic Models"
    http://psiexp.ss.uci.edu/research/papers/composite.pdf

  * Ramage et al. "Labeled LDA"
    http://nlp.stanford.edu/pubs/llda-emnlp09.pdf

  * Rosen-Zvi et al. "The Author-Topic Model for Authors and
    Documents" http://www.datalab.uci.edu/author-topic/398.pdf

  * Wallach "Topic Modeling: Beyond Bag-of-Words"
    http://www.cs.umass.edu/~wallach/publications/wallach06topic.pdf

  * Wallach et al. "Evaluation Methods for Topic Models"
    http://www.cs.umass.edu/~mimno/papers/wallach09evaluation.pdf