ACL 2017 - P17-1072

1. FRIENDSHIPS
RIVALRIES
TRYSTS
Chenhao Tan
Dallas Card (CMU), Noah Smith (UW)
1

2. RELATIONS
2
pro-choice pro-life
rivals
undocumented
immigrants
illegal alienrivals
small
government
free market
friends
word
alignment
machine
translation
friends
Chong and Druckman, 2007; Dawkins 1976; Entman, 1993;
Gitlin, 1980; Lakoff, 2014; Milton 1964

3. 3
undocumented
immigrants
illegal
alien
rivals
small
government
free
market
friends
First quantitative framework to systematically
describe relations between ideas
Demonstrate effective explorations with this
framework on a wide range of datasets

4. • Topics as ideas
• Keywords as ideas
4
Our focus is on
relations between ideas.
We will use standard approaches
• Topics from latent Dirichlet
allocation (Blei et al. 2003)
• Keywords (Monroe et al.
2008)
Hall et al. 2008
Culturomics, Michel et al. 2011

5. QUANTITATIVELY
• Given a corpus of documents over time,
each document consists of a set of ideas
5
Cooccurrence
Pointwise mutual information
[Church and Hanks 1990]
Rarely cooccur
undocumented
immigrants
illegal alienrivals

6. • Given a corpus of documents over time,
each document consists of a set of ideas
– Cooccurrence does not capture which is
winning or losing
6
Pearson correlation
QUANTITATIVELY
frequency
time
undocumented
immigrants
illegal alien

7. • Given a corpus of documents over time,
each document consists of a set of ideas
7
QUANTITATIVELY
Cooccurrence Prevalence
correlation
Within-
document
Across-
document
&

8. RARELY COOCCUR
8
1980 1990 2000 2010
immigrant,
undocumented
illegal, alien

9. 9Rarely cooccur
Always cooccur
CorrelatedAnti-correlated
Friendship
Arms-race
Tryst
Head-to-head

10. 10Rarely cooccur
Always cooccur
CorrelatedAnti-correlated
Friendship
Arms-race
Tryst
Head-to-head
Friendship

11. 11
1980 1990 2000 2010
immigrant,
undocumented
obama, president
LIKELY TO COOCCUR

12. 12Rarely cooccur
Always cooccur
CorrelatedAnti-correlated
Friendship
Arms-race
Tryst
Head-to-head

13. 13Rarely cooccur
Always cooccur
CorrelatedAnti-correlated
Friendship
Arms-race
Tryst
Head-to-head Arms-race

14. RARELY COOCCUR
14
1980 1990 2000 2010
immigration, deportation
republican, party

15. 15Rarely cooccur
Always cooccur
CorrelatedAnti-correlated
Friendship
Arms-race
Tryst
Head-to-head

16. 16Rarely cooccur
Always cooccur
CorrelatedAnti-correlated
Friendship
Arms-race
Tryst
Head-to-head Arms-race
Tryst

17. LIKELY TO COOCCUR
17
1980 1990 2000 2010
immigration, deportation
detainee, detention

18. 18
Rarely cooccur
Always cooccur
CorrelatedAnti-correlated
Friendship
Arms-race
Tryst
Head-to-head
We have shown a framework to quantitatively
describe relations between ideas.
Can we use them to effectively explore relations
between ideas?

19. • Newspapers and research articles as datasets
– Immigration
– Terrorism
– Same-sex marriage
– Abortion
– Tobacco
– ACL
– NIPS
19

20. 20
-1.0 -0.5 0.0 0.5 1.0
prevalence correlation
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
cooccurrence
pearsonr = 0.55
Correlated, but many
pairs in all four quadrants!

21. 21
Strength = |PMI| × |correlation|
Extreme pairs are
the interesting ones!
-1.0 -0.5 0.0 0.5 1.0
prevalence correlation
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
cooccurrence
pearsonr = 0.55

22. • Terrorism
– Keywords
– Topics
22

23. • Terrorism
– Keywords
– Topics
23

24. 24
1980 1990 2000 2010
0
0.1
0.2
0.3
frequency
arab
islam

25. • Terrorism
– Keywords
– Topics
25

26. 26
The relations between these topics are consistent with
structural balance theory: the enemy of an enemy is a friend
[Cartwright and Harary, 1956; Heider, 1946]

27. 27
PMI Correlation Joint
Keywords arab islam 106 1,494 2
Topics
federal, state
afghanistan,
taliban
43 99 2
federal, state iran, lybia 36 56 2
Rank among all relations
The “interesting” pair is ranked much
higher according to our framework.

28. 28
1980 1990 2000 2010
machine translation
sentiment analysis
Arms-race
1980 1990 2000 2010
machine translation
discourse (coherence)
Head-to-head
1980 1990 2000 2010
machine translation
rule,forest methods
Tryst
1980 1990 2000 2010
machine translation
word alignment
Friendship
Rarely cooccur
Always cooccur
CorrelatedAnti-correlated

29. 29https://github.com/nwrush/Visualization

30. 30
Thank you!
chenhao@chenhaot.com,
Twitter: @ChenhaoTan
Data & code:
https://chenhaot.com/papers/idea-relations.html
cooccurrence
prevalence
correlation
A quantitative way to describe
relations between ideas:
friendships, head-to-head,
arms-race, tryst
An effective framework to explore
temporal text corpora