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. They also investigated differences and similarities among various CL conferences (ACL, EMNLP, and COLING).
Some of their findings include :

• There is an increase in research in probabilistic models starting from late 80s (1988).
• There is a decline in research in semantics between 1978 and 2001, and possibly trending again after 2001.
• There is a steady increase in research in applications (MT, Speech Recognition, etc.) over time.
• COLING is a more diverse conference compare to ACL and EMNLP, but all three are becoming broader.
• Topics in ACL, COLING, and EMNLP conferences are converging (similarities of topics in these conferences are increasing over time).

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:
${\hat {p}}(z|y)=\sum _{d:t_{d}=y}{\hat {p}}(z|d){\hat {p}}(d|y)$
where $t_{d}$ is the date of document d, y is current year, and z is a topic.

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

This is only a subset of their results. There are more interesting plots 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