1. A Joint Model of Implicit Arguments for Nominal Predicates Matthew Gerber and Joyce Y. Chai Department of Computer Science Michigan State University East Lansing, Michigan, USA {gerberm2,jchai}@cse.msu.edu Language & Interaction Research Robert Bart Computer Science and Engineering University of Washington Seattle, 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. 2 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. 3

4. Implicit Arguments What can traditional SRL systems tell us? –Who is the producer? –What is produced? –What is manufactured? But thats 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. 4

5. Implicit Arguments What can traditional SRL systems tell us? –Who is the producer? –What is produced? –What is manufactured? But thats 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. 5

6. Implicit Arguments What can traditional SRL systems tell us? –Who is the producer? –What is produced? –What is manufactured? But thats 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. 6

7. Implicit Arguments What can traditional SRL systems tell us? –Who is the producer? –What is produced? –What is manufactured? But thats 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. 7

8. Model Formulation (Gerber and Chai, 2010) Candidate selection –PropBank/NomBank arguments –Two-sentence candidate window Coreference chaining – Binary classification function – c2 c3 c1 Georgia-Pacific and Nekoosa produce market pulp, containerboard and white paper. The goods could be manufactured closer to customers, saving shipping costs. 8 Assume independent arguments

9. Are Arguments Independent? The president is struggling to manage the countrys economy. If he cannot get it under control, loss of the next election might result. 9

10. 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 countrys economy. If he cannot get it under control, loss of the next election might result. 10

11. 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 11

12. 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 12

13. Joint Implicit Argument Model The president is struggling to manage the countrys economy. If he doesnt succeed by the next election, a loss might result. 13

14. Joint Implicit Argument Model Match rank Match similarity Local model scores 14

15. Evaluation Setting Data created by Gerber and Chai (2010) –1,200 annotations of 10 predicates Only test instances that take iarg 0 and iarg 1 Ten-fold cross-validation Baseline: independent classification model 15

16. 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 Georgia-Pacific and Nekoosa produce market pulp, containerboard and white paper. The goods could be manufactured closer to customers, saving shipping costs. 16

17. Evaluation Results Overall results –Baseline F 1 : 72.2% –Joint F 1 : 73.1% Per-predicate PredicateBaseline F 1 (%)Joint F 1 (%) bid66.778.2 investment53.362.5 17

18. 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 [iarg 1 money] 18

19. 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: –Answers: money, amount, million Query 2: –Answers: government, business, investor [iarg 1 money] 19

20. 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 20

21. Future Work Extension to other predicates –Only 10 are currently considered Extension to other argument positions –iarg 2 and iarg 3 are also common Computational complexity –Exhaustive search is intractable –Heuristic search –Gibbs sampling for joint inference 21

22. Questions? Matthew Gerber: gerberm2@msu.edu 22