Hall emnlp2008

Citation

• 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

Instead of using dynamic topic models, they used static Topic_model (vanilla LDA) with post hoc analysis to calculate observed probability of topics in the current year, computed as follows:
${\hat {p}}(z|y)=\sum _{d:t_{d}=y}{\hat {p}}(z|d){\hat {p}}(d|y)$

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

These is only a subset of their results. There are more in the paper.

• Trending topics in the CL community
  

• Declining topics in the CL community
  

• 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