Difference between revisions of "Hall emnlp2008"

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\hat{p}(z|y) = \sum_{d:t_d=y} \hat{p}(z|d) \hat{p}(d|y)
 
\hat{p}(z|y) = \sum_{d:t_d=y} \hat{p}(z|d) \hat{p}(d|y)
 
</math>
 
</math>
I is the indication function, t_d is the date document d was published, p(d|y) is a constant 1/C
 
  
 
== Experiments ==
 
== Experiments ==

Revision as of 14:55, 1 April 2011

Paper

  • Title : Studying the History of Ideas Using Topic Models
  • Authors : D. Hall, D. Jurafsky, and C. D. Manning
  • Venue : EMNLP 2008

Summary

This paper uses topic models to study the development of ideas over time for papers in computational linguistics conferences (ACL, COOLING, EMNLP, etc.)

Dataset

ACL Anthology (~12,500 papers)

Model

LDA with post hoc analysis to calculate observed probability of topics in the current year

Experiments

  • Ran 100 topics LDA, took relevant 36 topics.
  • Seeded words for 10 more topics to improve coverage.
  • Used these 36+10 topics as priors for new 100-topics run.
  • Picked 43 topics and manually labeled them.

Results

  • Trending topics in the CL community

Halltrend.png

  • Declining topics in the CL community

Halltdecline.png

  • NLP applications

They investigated whether CL is becoming more applied over time.
They explored six applicatons : Machine Translation, Spelling Correction, Dialogue Systems, Call Routing, Speech Recognition, and Biomedical
Hallapp.png

  • ACL vs COLING vs EMNLP

File:Hallacl.png