Difference between revisions of "Compare Yano et al NAACL 2009 Link PLSA LDA"
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== Comparison == | == Comparison == | ||
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+ | Both of these papers are extensions of [[Mixed_membership_models_of_scientific_publication | Link-LDA]] and use a blog dataset. Yano et al. tries to predict which user will comment on a blog posting whereas Nallapati and Cohen try to predict which blog will link to another blog. | ||
=== Method === | === Method === |
Revision as of 20:40, 1 December 2012
Contents
Papers
The papers are:
Comparison
Both of these papers are extensions of Link-LDA and use a blog dataset. Yano et al. tries to predict which user will comment on a blog posting whereas Nallapati and Cohen try to predict which blog will link to another blog.
Method
Datasets Used
- Yano et al. uses a corpus of blog posts from 40 different blog sites focusing on American politics during from November 2007 to October 2008 (right up to a presidential election). Diversity in political leanings was emphasized for the final selection. Five blogs were chosen for the final selection.
- Nallapati and Cohen also use a corpus of blogs, but these were collected from July 2004 - July 2005. Initially, it was a noisy dataset with lots of broken links and useless information. The authors constrained blogs used to have a minimum of 2 ingoing or 2 outgoing links.
Problem
Big Idea
Other
Questions
- How much time did you spend reading the (new, non-wikified) paper you summarized? About 2 hours
- How much time did you spend reading the old wikified paper? About 2 hours
- How much time did you spend reading the summary of the old paper? About 15 min
- How much time did you spend reading background material? N/A My final project for the class is on this area so I've read a lot of background papers
- Was there a study plan for the old paper? Yes
- if so, did you read any of the items suggested by the study plan? and how much time did you spend with reading them? I had actually read the papers before as it is directly related to my research with my advisor. I do a lot of Gibbs Sampling on graphical models (in particular topic-model derivatives) and that fits into the study plan
- Give us any additional feedback you might have about this assignment. I like this comparison. It was a nice way to view the papers in a different light and really made it stick in my memory. In general, I like the wikifying and used it extensively for the project (and probably will use this for my research in the future after the class is over).