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Bagging

Bagging. LING 572 Advanced Statistical Methods in NLP Presentation March 9, 2006 David Bullock Lap Cheung. Bagging – System Overview. From each training sample (1K, 5K, 10K) we created 10 bootstrap samples Trained 3 taggers (Trigram, TBL, MaxEnt) on each bootstrap sample.

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Bagging

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  1. Bagging LING 572 Advanced Statistical Methods in NLP Presentation March 9, 2006 David Bullock Lap Cheung

  2. Bagging – System Overview • From each training sample (1K, 5K, 10K) we created 10 bootstrap samples • Trained 3 taggers (Trigram, TBL, MaxEnt) on each bootstrap sample LING 572 Bullock & Cheung

  3. Bagging - Example • Input: • Rolls-Royce/NNP Motor/NNP Cars/NNPS... • The/DT luxury/NN auto/NN maker/NN... • Investors/NNS are/VBP appealing/VBG to/TO... • According/VBG to/TO some/DT estimates/NNS • Output: • Rolls-Royce/NNP Motor/NNP Cars/NNPS... • Rolls-Royce/NNP Motor/NNP Cars/NNPS... • The/DT luxury/NN auto/NN maker/NN... • According/VBG to/TO some/DT estimates/NNS LING 572 Bullock & Cheung

  4. Bagging – System Overview (2) • Tagged the test data. • Combined the results by voting LING 572 Bullock & Cheung

  5. Every vote counts • Majority takes all (for each word/tag pair) • If draw, consider previous word/tag pairs • Consider • only the last word/tag pair • all previous pairs (the whole history) • nothing (just randomly pick a tag if draw) LING 572 Bullock & Cheung

  6. Voting - Example • Voting input from three files: • John/NN will/MD join/VB the/DT board/NNP • John/NNP will/MD join/VB the/DT board/NN • John/NNP will/NN join/VB the/DT board/VB • Voting output: • John/NNP will/MD join/VB the/DT board/?? LING 572 Bullock & Cheung

  7. How to count votes? • Use ‘count wins for ALL previous word/tag pairs’ LING 572 Bullock & Cheung

  8. Bagging Effect • Varied # of bags • Observed the effects on • Taggers • Training Data Size LING 572 Bullock & Cheung

  9. Trigram Tagger LING 572 Bullock & Cheung

  10. TBL Tagger LING 572 Bullock & Cheung

  11. MaxEnt Tagger LING 572 Bullock & Cheung

  12. Result Combination LING 572 Bullock & Cheung

  13. Result Combination (1K) LING 572 Bullock & Cheung

  14. Result Combination (5K) LING 572 Bullock & Cheung

  15. Result Combination (10K) LING 572 Bullock & Cheung

  16. Bagging – Further Improvement • Do not use “bad” bags • A “bad” bag: using such a bag decreases the marginal accuracy • Example: Bag 4 and Bag 7 in 5K/10K data • Improve the accuracy by 0.015% - 0.02% LING 572 Bullock & Cheung

  17. Conclusion • Combinations of multiple-tagger results by voting always gave better results than working with a single tagger • When using a single parser and 10 bags, bagging gave better results for the TBL and MaxEnt taggers, but not for the Trigram tagger • Tagging accuracy can be further improved by removing “bad” bags LING 572 Bullock & Cheung

  18. Q & A LING 572 Bullock & Cheung

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