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System Combination

System Combination. LING 572 Fei Xia 01/31/06. Papers. (Henderson and Brill, EMNLP-1999): Exploiting Diversity in NLP: Combining Parsers (Henderson and Brill, ANLP-2000): Bagging and Boosting a Treebank Parser. f 1. f. f 2. …. f m. Task. Paper #1. ML 1. f 1. ML 2. f 2. f. ML m.

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System Combination

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  1. System Combination LING 572 Fei Xia 01/31/06

  2. Papers • (Henderson and Brill, EMNLP-1999): Exploiting Diversity in NLP: Combining Parsers • (Henderson and Brill, ANLP-2000): Bagging and Boosting a Treebank Parser

  3. f1 f f2 … fm Task

  4. Paper #1 ML1 f1 ML2 f2 f MLm fm

  5. Paper #2: bagging ML f1 ML f2 f ML fm

  6. Combining parsers

  7. Scenario ML1 f1 ML2 f2 f MLm fm

  8. Three parsers • Collins (1997) • Charniak (1997) • Ratnaparkhi (1997)

  9. Major strategies • Parse hybridization: combine substructures of the input parses to produce a better parse. • Parser switching: for each x, f(x) is one of the fi(x)

  10. Parse hybridization: Method 1 • Constituent voting: • Include a constituent if it appears in the output of a majority of the parsers. • It requires no training. • All parsers are treated equally.

  11. Parse hybridization: Method 2 • Naïve Bayes Y=π(c) is a binary function return true when c should be included in the hyp Xi=Mi(c) is a binary function return true when parser i suggests c should be in the parse

  12. Parse hybridization • If the number of votes required by constituent voting is greater than half of the parsers, the resulting structure has no crossing constituents. • What will happen if the input parsers disagree often?

  13. Parser switching: Method 1 • Similarity switching • Intuition: choose the parse that is most similar to the other parses. • Algorithm: • For each parse πi, create the constituent set Si. • The score for πi is • Choose the parse with the highest score. • No training is required.

  14. Parser switching: Method 2 • Naïve Bayes

  15. Experiments • Training data: WSJ except sections 22 and 23 • Development data: Section 23 • For training Naïve Bayes • Test data: Section 22

  16. Parsing results

  17. Robustness testing 90.43 90.74 91.25 91.25 Add a 4th parser: F-measure about 67.6 Performance remains the same except for constituent voting

  18. Summary of 1st paper • Combining parsers produces good results: 89.67%  91.25% • Different methods of combining: • Parse hybridization • Constituent voting • Naïve Bayes • Parser switching • Similarity switching • Naïve Bayes

  19. Bagging and Boosting a Treebank Parser

  20. Experiment settings • Parser: Collins’s Model 2 (1997) • Training data: sections 01-21 • Test data: Section 23

  21. Bagging ML f1 ML f2 f {(s,t)} ML fm Combining method: constituent voting

  22. Experiment results • Baseline (no bagging): 88.63 • Initial (one bag): 88.38 • Final (15 bags): 89.17

  23. Training corpus size effects

  24. Boosting Weighted Sample ML f1 Training Sample ML Weighted Sample f2 f … ML fT

  25. Boosting results Boosting does not help: 88.63  88.84

  26. Summary • Combining parsers produces good results: 89.67%  91.25% • Bagging helps: 88.63%  89.17% • Boosting does not help (in this case): 88.63%  88.84%

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