HW6: Literature Survey
out 2/23, due 3/7, 3/16 and 4/4
This is the second step in the process of writing your research paper: a document that defines your topic or problem and summarizes existing work on it in two to five pages along with an annotated bibliography of significant papers on it that you have read. There are three parts to the assignment.
- By 7 March, identify some key conferences and journals where papers on your topic are published and select the 10-20 papers you will read.
- By 16 March, produce an annotated bibliography for the selected papers, perhaps after eliminating some that turned out to be poor choices and adding some replacements that you discovered along the way. You should have one or two paragraphs for each paper that summarize the research done, contributions made and a brief evaluation.
- By 4 April, write a final literature summary for the area that includes two to four pages organizing and describing the work done on the topic or problem along with your annotated bibliography. You will give this to your reader and ask him or her to review it and send me feedback using a form that will be provided.
Finding Relevant Papers
- Google is a useful tool, but should not
be the only place you
look! Also, as you get to know the field you're investigating, you
can keep going back to google (and other sources) with new keywords
(buzzwords) you've discovered.
- CiteSeer, an online
citation index and paper database originally developed by NEC, is a terrific
resource. Many of the top google hits will likely be to CiteSeer
papers. In CiteSeer, you can search by keyword/title/author, and can
follow citation links forward and backward from important papers.
There are also other nifty features like semantically similar papers,
and "importance" measured based on number of citations.
Many papers have associated (but possibly incomplete/incorrect) BibTeX
entries.
- For AI papers, if you really don't know where to start, AI Topics is
sometimes helpful. It's a AAAI-maintained website that has some
introductory material, a collection of links on various topics, news
articles, and other interesting pointers. But the actual content is
on the non-technical side (it's more at the high-school term-paper
level), so it should just be one resource, not your
primary one.
- Identify a few important and relevant (recent or classic/seminal)
papers, and work forwards and backwards through citation links
(following references in the paper, and looking in CiteSeer to see who
later cited this paper).
- It's important to know what are the key publications (top
journals and conferences) and researchers (most published and cited
authors) in your field of interest. You may be able to find this out
early in the process by asking somebody who's knowledgeable, or by
stumbling on (or starting from) the key seminal paper(s). Or you may
have to discover it more gradually. (If you see a citation or a name
repeatedly, look it up! It's important! But also note:
"important" is not synonymous with "good" -- sometimes everybody cites
a paper just because everybody else does, not because it's actually a
particularly good or useful paper.)
- Also pay attention to institutions -- you'll quickly learn which
places are doing important work in your area. (And my list of top
institutions for AI won't be the same as your list of top institutions
for graphics, or whatever. In fact, my list of top institutions for
my particular subareas of AI might not be the same as that of some other AI
researcher with a different focus. Of course, these things are
subjective, so my list might not even be the same as that of a
researcher with the same focus...)
Locating and Reading Papers
- If you can't find a paper online, try the library, other students,
your advisor or outside reader. (For example, I have most volumes of
the Machine Learning Journal in my office. Also, as a AAAI member, I have
online access to all AAAI publications.)
- Be sure you use your time wisely, using the paper-reading tips
we've already gone over to figure out which papers are worth reading
closely, and which only deserve a cursory review.
- Don't make the
mistake of "depth-first search" of the paper space. Instead, look at
papers closely enough to know how important they are, and to start
creating clusters of "similar" papers (i.e., identifying themes,
threads, or styles of research within the field). Then organize your
reading by clusters. This is a much more efficient way to read than a
scattershot approach. Also, doing this as you go along will help you
to organize the literature survey itself.
- As you read papers, make note of important citations to follow up
on (and what cluster those citations seem to belong to).
- Know when to stop. There will be more related fields than you can
possibly follow up on. Be prepared to say "Researchers in statistics
and physics also touch on these topics," perhaps (if you're lucky)
with a pointer to relevant survey papers on those areas, and
leave it at that.
- Take notes and keep them in an organized system -- notebook,
online, whatever. Don't just scribble on pieces of paper. I like to
make notes in the margins of papers, but also to write short bulleted
summaries of papers that are particularly relevant for an area that
I'm trying to synthesize.
Some examples of review papers
Here are some examples of lierature review papers. Note that these are high-quality, published papers so they are much more extensive and polichsed than what we expect from you for this homework. However, you can look at them to see how much effort goes into organizing research in a sub-sub-area.
- T.Todd Elvins, A Survey of Algorithms for Volume Visualization,
Computer Graphics, Volume 26, Number 3, August 1992.
- A.K. Jain, M.N. Murty, and P.J. Flynn. 1999. Data clustering: a
review. ACM Comput. Surv. 31, 3 (September 1999), 264-323.
- Renzo Angles and Claudio Gutierrez. 2008. Survey of graph database models. ACM Comput. Surv. 40, 1, Article 1 (February 2008).
You might browse or search through the papers published in the ACM journal Computing Surveys. Their mission: "Computing Surveys does not publish ``new'' research. This is left to the Transactions and other specialized publications of the ACM. Instead, Computing Surveys focuses on surveys and tutorials that integrate the existing literature and put its results in context."
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