Learning and Inference for Hierarchical Parsing: A Comprehensive Overview

1. Learning and Inference for Hierarchically Split PCFGs Slav Petrov and Dan Klein

2. The Game of Designing a Grammar • Annotation refines base treebank symbols to improve statistical fit of the grammar • Parent annotation [Johnson ’98]

3. The Game of Designing a Grammar • Annotation refines base treebank symbols to improve statistical fit of the grammar • Parent annotation [Johnson ’98] • Head lexicalization[Collins ’99, Charniak ’00]

4. The Game of Designing a Grammar • Annotation refines base treebank symbols to improve statistical fit of the grammar • Parent annotation [Johnson ’98] • Head lexicalization[Collins ’99, Charniak ’00] • Automatic clustering?

5. Forward X1 X7 X2 X4 X3 X5 X6 . He was right Backward [Matsuzaki et al. ‘05] Learning Latent Annotations EM algorithm: • Brackets are known • Base categories are known • Only induce subcategories Just like Forward-Backward for HMMs.

6. Limit of computational resources Overview - Hierarchical Training - Adaptive Splitting - Parameter Smoothing

7. DT-2 DT-3 DT-1 DT-4 Refinement of the DT tag DT

8. Refinement of the DT tag DT

9. Hierarchical refinement of the DT tag DT

10. Hierarchical Estimation Results

11. Refinement of the , tag • Splitting all categories the same amount is wasteful:

12. Likelihood with split reversed Likelihood with split Adaptive Splitting • Want to split complex categories more • Idea: split everything, roll back splits which were least useful

13. Likelihood with split reversed Likelihood with split Adaptive Splitting • Want to split complex categories more • Idea: split everything, roll back splits which were least useful

14. Adaptive Splitting Results

15. Number of Phrasal Subcategories

16. Number of Phrasal Subcategories NP VP PP

17. Number of Phrasal Subcategories NAC X

18. Number of Lexical Subcategories POS TO ,

19. Number of Lexical Subcategories NNP JJ NNS NN

20. Smoothing • Heavy splitting can lead to overfitting • Idea: Smoothing allows us to pool statistics

21. Result Overview

22. Linguistic Candy • Proper Nouns (NNP): • Personal pronouns (PRP):

23. Linguistic Candy • Relative adverbs (RBR): • Cardinal Numbers (CD):

24. Inference She heard the noise. Exhaustive parsing: 1 min per sentence

25. Treebank Coarse grammar Prune Parse Parse NP … VP NP-1 VP-6 NP-17 … VP-31 NP-12 … Refined grammar [Goodman ‘97, Charniak&Johnson ‘05] Coarse-to-Fine Parsing

26. Hierarchical Pruning < t Consider again the span 5 to 12: coarse: split in two: split in four: split in eight:

27. G1 G2 G3 G4 G5 G6 DT DT1 DT2 DT1 DT2 DT3 DT4 Learning DT1 DT2 DT3 DT4 DT5 DT6 DT7 DT8 Intermediate Grammars X-Bar=G0 G=

28. 0(G) 1(G) 2(G) 3(G) 4(G) 5(G) G1 G2 G3 G4 G5 G6 G1 G2 G3 G4 G5 G6 Learning Learning Projection i G Projected Grammars X-Bar=G0 G=

29. Final Results (Efficiency) • Parsing the development set (1600 sentences) • Berkeley Parser: • 10 min • Implemented in Java • Charniak & Johnson ‘05 Parser • 19 min • Implemented in C

30. Final Results (Accuracy)

31. Extensions • Acoustic modeling • Infinite Grammars • Nonparametric Bayesian Learning [Petrov, Pauls & Klein ‘07] [Liang, Petrov, Jordan & Klein ‘07]

32. Conclusions • Split & Merge Learning • Hierarchical Training • Adaptive Splitting • Parameter Smoothing • Hierarchical Coarse-to-Fine Inference • Projections • Marginalization • Multi-lingual Unlexicalized Parsing

33. Thank You! http://nlp.cs.berkeley.edu