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ChronoSAGEChronoSAGE:
Diversifying Topic Modeling
Chronologically
Tomonari MASADA
NAGASAKI University
masada@nagasaki-u.ac.jp
Solution
ProblemProblem
• Find research trends
• Present them in a readable manner
Solution
• Extract trending words at each epoch
• Display them chronologically
MethodMethod
•SAGE [Eisenstein+ 11]
–Represent each word probability
as a multiplication of factors
ChronoSAGE
• Use SAGE for our chronological
analysis of academic papers
• Represent each word probability
as a multiplication of four factors
ChronoSAGE
• Use SAGE for our chronological
analysis of time-stamped docs
• Represent each word probability
as a multiplication of four factors
corpus-wide
background
per-topic
background
per-epoch
background
per-topic
trends
words sorted by
per-epoch background probabilities (TDT4)
t=0 edt paralymp lebanon 32nd wild-card u.s china
t=1 kippur 10-13 lebanon china palestinian text join
t=2 10-14 10-16 10-18 10-15 10-19 10-17 10-20
t=3 10-24 10-23 10-22 10-25 10-21 10-26 10-27
t=4 10-29 10-28 10-31 10-30 11-3 leipzig lebanon
t=5 11-10 11-8 11-9 11-6 11-7 11-5 convuls
t=6 11-17 11-16 11-11 11-14 11-15 11-12 11-13
t=7 11-18 11-19 11-24 11-22 11-23 11-20 11-21
t=8 11-25 11-27 11-28 11-26 11-30 11-29 seclus
words sorted by
per-epoch background probabilities (TDT4)
t=9 12-8 12-6 12-5 12-7 12-3 537-vote 12-4
t=10 12-12 12-15 12-14 12-10 12-13 12-11 12-9
t=11 12-17 12-18 12-21 12-20 12-19 12-22 12-16
t=12 12-24 12-28 12-29 12-23 12-27 12-26 12-25
t=13 309 tabasco 2001 1-5 vy 12-0 free-agent
t=14 presid-elect’s 1-12 1-8 1-11 1-9 1-10 1-7
t=15 1-14 1-13 1-19 1-18 1-17 1-16 1-15
t=16 1-21 1-26 1-25 1-22 1-20 1-23 1-24
t=17 1-28 1-31 1-30 1-27 1-29 dawosi bhuj
Evaluation (1)
• SAGE and ChronoSAGE are better
than LDA in terms of PMI (point-
wise mutual information).
–We used the entire English
Wikipedia for PMI computation.
PMI
PMI 𝑤𝑖, 𝑤𝑗 = log
𝑝(𝑤 𝑖,𝑤 𝑗)
𝑝(𝑤 𝑖)𝑝(𝑤 𝑗)
,
where 𝑝 𝑤𝑖 = 𝑅𝑖/𝑅.
Evaluation (2)
• ChronoSAGE can extract
chronological trends for each topic
as top-K word lists.
–ChronoSAGE can do what SAGE can’t do.
ChronoSAGE: Diversifying Topic Modeling Chronologically
ChronoSAGE: Diversifying Topic Modeling Chronologically
ChronoSAGE: Diversifying Topic Modeling Chronologically
ChronoSAGE: Diversifying Topic Modeling Chronologically

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ChronoSAGE: Diversifying Topic Modeling Chronologically

  • 1. ChronoSAGEChronoSAGE: Diversifying Topic Modeling Chronologically Tomonari MASADA NAGASAKI University masada@nagasaki-u.ac.jp
  • 2. Solution ProblemProblem • Find research trends • Present them in a readable manner Solution • Extract trending words at each epoch • Display them chronologically
  • 3. MethodMethod •SAGE [Eisenstein+ 11] –Represent each word probability as a multiplication of factors
  • 4. ChronoSAGE • Use SAGE for our chronological analysis of academic papers • Represent each word probability as a multiplication of four factors ChronoSAGE • Use SAGE for our chronological analysis of time-stamped docs • Represent each word probability as a multiplication of four factors
  • 7. words sorted by per-epoch background probabilities (TDT4) t=0 edt paralymp lebanon 32nd wild-card u.s china t=1 kippur 10-13 lebanon china palestinian text join t=2 10-14 10-16 10-18 10-15 10-19 10-17 10-20 t=3 10-24 10-23 10-22 10-25 10-21 10-26 10-27 t=4 10-29 10-28 10-31 10-30 11-3 leipzig lebanon t=5 11-10 11-8 11-9 11-6 11-7 11-5 convuls t=6 11-17 11-16 11-11 11-14 11-15 11-12 11-13 t=7 11-18 11-19 11-24 11-22 11-23 11-20 11-21 t=8 11-25 11-27 11-28 11-26 11-30 11-29 seclus
  • 8. words sorted by per-epoch background probabilities (TDT4) t=9 12-8 12-6 12-5 12-7 12-3 537-vote 12-4 t=10 12-12 12-15 12-14 12-10 12-13 12-11 12-9 t=11 12-17 12-18 12-21 12-20 12-19 12-22 12-16 t=12 12-24 12-28 12-29 12-23 12-27 12-26 12-25 t=13 309 tabasco 2001 1-5 vy 12-0 free-agent t=14 presid-elect’s 1-12 1-8 1-11 1-9 1-10 1-7 t=15 1-14 1-13 1-19 1-18 1-17 1-16 1-15 t=16 1-21 1-26 1-25 1-22 1-20 1-23 1-24 t=17 1-28 1-31 1-30 1-27 1-29 dawosi bhuj
  • 9. Evaluation (1) • SAGE and ChronoSAGE are better than LDA in terms of PMI (point- wise mutual information). –We used the entire English Wikipedia for PMI computation.
  • 10. PMI PMI 𝑤𝑖, 𝑤𝑗 = log 𝑝(𝑤 𝑖,𝑤 𝑗) 𝑝(𝑤 𝑖)𝑝(𝑤 𝑗) , where 𝑝 𝑤𝑖 = 𝑅𝑖/𝑅.
  • 11.
  • 12.
  • 13.
  • 14. Evaluation (2) • ChronoSAGE can extract chronological trends for each topic as top-K word lists. –ChronoSAGE can do what SAGE can’t do.