a joint model of implicit arguments for nominal predicates matthew gerber and joyce y. chai...

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

rbart@cs.washington.edu

2

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?

3

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

9

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

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Questions?

Matthew Gerber: gerberm2@msu.edu