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FRIENDSHIPS RIVALRIES TRYSTS
Chenhao Tan Dallas Card (CMU), Noah Smith (UW)
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RELATIONS
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FRIENDSHIPS RIVALRIES TRYSTS Chenhao Tan Dallas Card (CMU), Noah - - PowerPoint PPT Presentation
FRIENDSHIPS RIVALRIES TRYSTS Chenhao Tan Dallas Card (CMU), Noah - - PowerPoint PPT Presentation
FRIENDSHIPS RIVALRIES TRYSTS Chenhao Tan Dallas Card (CMU), Noah Smith (UW) 1 RELATIONS rivals pro-choice pro-life undocumented rivals illegal alien immigrants friends small free market government word machine friends alignment
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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
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- Topics as ideas
- Keywords as ideas
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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
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QUANTITATIVELY
- Given a corpus of documents over time,
each document consists of a set of ideas
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Cooccurrence Pointwise mutual information [Church and Hanks 1990]
Rarely cooccur undocumented immigrants illegal alien rivals
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- Given a corpus of documents over time,
each document consists of a set of ideas
– Cooccurrence does not capture which is winning or losing
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Pearson correlation
QUANTITATIVELY
frequency time undocumented immigrants illegal alien
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- Given a corpus of documents over time,
each document consists of a set of ideas
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QUANTITATIVELY
Cooccurrence Prevalence correlation Within- document Across- document &
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RARELY COOCCUR
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1980 1990 2000 2010
immigrant, undocumented illegal, alien
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Rarely cooccur Always cooccur Correlated Anti-correlated Friendship Arms-race Tryst Head-to-head
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Rarely cooccur Always cooccur Correlated Anti-correlated Friendship Arms-race Tryst Head-to-head Friendship
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1980 1990 2000 2010
immigrant, undocumented
- bama, president
LIKELY TO COOCCUR
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Rarely cooccur Always cooccur Correlated Anti-correlated Friendship Arms-race Tryst Head-to-head
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Rarely cooccur Always cooccur Correlated Anti-correlated Friendship Arms-race Tryst Head-to-head Arms-race
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RARELY COOCCUR
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1980 1990 2000 2010
immigration, deportation republican, party
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Rarely cooccur Always cooccur Correlated Anti-correlated Friendship Arms-race Tryst Head-to-head
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Rarely cooccur Always cooccur Correlated Anti-correlated Friendship Arms-race Tryst Head-to-head Arms-race Tryst
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LIKELY TO COOCCUR
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1980 1990 2000 2010
immigration, deportation detainee, detention
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Rarely cooccur Always cooccur Correlated Anti-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?
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- Newspapers and research articles as datasets
– Immigration – Terrorism – Same-sex marriage – Abortion – Tobacco – ACL – NIPS
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- 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!
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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
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- Terrorism
– Keywords – Topics
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- Terrorism
– Keywords – Topics
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1980 1990 2000 2010 0.1 0.2 0.3
frequency
arab islam
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- Terrorism
– Keywords – Topics
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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]
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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.
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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 Correlated Anti-correlated
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https://github.com/nwrush/Visualization
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