a joint model of implicit arguments for nominal predicates matthew gerber and joyce y. chai...
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A Joint Model of Implicit Arguments for Nominal Predicates
Matthew Gerber and Joyce Y. ChaiDepartment of Computer Science
Michigan State UniversityEast Lansing, Michigan, USA
{gerberm2,jchai}@cse.msu.edu
Language &InteractionResearch
Robert BartComputer Science and Engineering
University of WashingtonSeattle, Washington, USA
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Implicit Arguments
Georgia-Pacific and Nekoosa produce market pulp,
containerboard and white paper. The goods could be
manufactured closer to customers, saving shipping costs.
• What can traditional SRL systems tell us?
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Implicit Arguments
• What can traditional SRL systems tell us?– Who is the producer?– What is produced?
Georgia-Pacific and Nekoosa produce market pulp,
containerboard and white paper. The goods could be
manufactured closer to customers, saving shipping costs.
4
Implicit Arguments
• What can traditional SRL systems tell us?– Who is the producer?– What is produced?– What is manufactured?
• But that’s not the whole story…– Who is the manufacturer?
Georgia-Pacific and Nekoosa produce market pulp,
containerboard and white paper. The goods could be
manufactured closer to customers, saving shipping costs.
5
Implicit Arguments
• What can traditional SRL systems tell us?– Who is the producer?– What is produced?– What is manufactured?
• But that’s not the whole story…– Who is the manufacturer?– Who ships?
Georgia-Pacific and Nekoosa produce market pulp,
containerboard and white paper. The goods could be
manufactured closer to customers, saving shipping costs.
6
Implicit Arguments
• What can traditional SRL systems tell us?– Who is the producer?– What is produced?– What is manufactured?
• But that’s not the whole story…– Who is the manufacturer?– Who ships what?
Georgia-Pacific and Nekoosa produce market pulp,
containerboard and white paper. The goods could be
manufactured closer to customers, saving shipping costs.
7
Implicit Arguments
• What can traditional SRL systems tell us?– Who is the producer?– What is produced?– What is manufactured?
• But that’s not the whole story…– Who is the manufacturer?– Who ships what to whom? Implicit arguments
Georgia-Pacific and Nekoosa produce market pulp,
containerboard and white paper. The goods could be
manufactured closer to customers, saving shipping costs.
8
Model Formulation (Gerber and Chai, 2010)
• Candidate selection– PropBank/NomBank arguments– Two-sentence candidate window
• Coreference chaining–
• Binary classification function–
c2
c3
c1
)... ,',iarg,shipping|Pr( 31 c
},{' 233 ccc
Georgia-Pacific and Nekoosa produce market pulp,
containerboard and white paper. The goods could be
manufactured closer to customers, saving shipping costs.
Assume independent arguments
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Are Arguments Independent?
The president is struggling to manage the country’s economy.
If he cannot get it under control, loss of the next election
might result.
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Are Arguments Independent?
• What entity might lose?– Economies lose jobs, value, etc.– Presidents lose votes, allegiance, etc.
• Implicit arguments are not independent• A joint model would be more natural
The president is struggling to manage the country’s economy.
If he cannot get it under control, loss of the next election
might result.
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Related Work
• Joint verbal SRL (Toutanova et al. (2008))– Re-rank full argument structures– Joint label sequence
• [arg0, Predicate, arg1]• [arg0, Predicate, arg0]
• Joint selectional preferences (Ritter et al. (2010))– [Arg0 economy] [Predicate lost] [Arg1 jobs]
– [Arg0 economy] [Predicate lost] [Arg1 election]
– Relies on TextRunner extraction system
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TextRunner
• Open Information Extraction (OIE) database• Query
– Arg0: ?– Predicate: lose– Arg1: election
• Answer– [Arg0 The president] [Predicate lost] [Arg1 the election].
• Use TextRunner to identify joint implicit arguments
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Joint Implicit Argument Model
• Model joint occurrence of iarg0 and iarg1
• Consider all possible candidate assignments
The president is struggling to manage the country’s economy.
If he doesn’t succeed by the next election, a loss might result.
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Joint Implicit Argument Model
• Using TextRunner queries
• Query 1: <president, lose, ?>1. <Kenyan president, lose, election>
2. <president, lose, ally>
3. …
• Query 2: <?, lose, election>1. <Republican party, lose, election>
2. <president, lose, election>
3. …
• Match rank• Match similarity• Local model scores
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Evaluation Setting
• Data created by Gerber and Chai (2010)– 1,200 annotations of 10 predicates
• Only test instances that take iarg0 and iarg1
• Ten-fold cross-validation• Baseline: independent classification model
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• Methodology (Ruppenhofer et al., 2010)
– Ground-truth implicit arguments:– Predicted implicit argument:– Prediction score:
– P: total prediction score / prediction count– R: total prediction score / true implicit positions
Evaluation Setting
p},,{ 1 nttT
),Dice(max),Score( iTt
tpTpi
Georgia-Pacific and Nekoosa produce market pulp,
containerboard and white paper. The goods could be
manufactured closer to customers, saving shipping costs.
17
Evaluation Results
• Overall results– Baseline F1: 72.2%
– Joint F1: 73.1%
• Per-predicatePredicate Baseline F1 (%) Joint F1 (%)
bid 66.7 78.2
investment 53.3 62.5
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Example Improvement
Big investors can decide to ride out market storms without
selling stock. They often do that because stocks have proved
to be the best-performing investment, attracting $1 trillion.
• What was invested?• Who invested?
– Baseline (independent) model is incorrect– Joint model is correct
[iarg1 money]
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Example Improvement
Big investors can decide to ride out market storms without
selling stock. They often do that because stocks have proved
to be the best-performing investment, attracting $1 trillion.
• Query 1: <investor, invest, ?>– Answers: money, amount, million
• Query 2: <?, invest, money>– Answers: government, business, investor
[iarg1 money]
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Summary
• Implicit arguments– Frequent– Nearby– Can be automatically recovered
• Semantic arguments are not independent– OIE can help identify argument dependencies– Joint model can recover from simple errors
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Future Work
• Extension to other predicates– Only 10 are currently considered
• Extension to other argument positions– iarg2 and iarg3 are also common
• Computational complexity– Exhaustive search is intractable– Heuristic search– Gibbs sampling for joint inference
