1. Modeling Consensus: Classifier Combination for WSD Authors: Radu Florian and David Yarowsky Presenter: Marian Olteanu

2. Introduction Ensembles (classifier combination)  If errors are uncorrelated, decrease error by a factor of 1/N  In practice, all classifiers tend to make errors at hard examples

3. Approach & Features Automatic POS tagging and lemma extraction Features  Bag of words  Local  Syntactic

4. Classifier methods (6) Vector-based  Enhanced Naïve Bayes Weighted  Cosine  BayesRatio (good for sparse data)

5. Classifier methods (cont.) MMVC (Mixture Maximum Variance Correction)  2 stages  Second stage: select sense with variance over threshold

6. Classifier methods (cont.) Discriminative Models  TBL (Transformation Based Learning)  Non-hierarchical decision lists

7. Combining classifiers Agreement

8. Combining classifiers (cont.) Three methods 1. Combine posterior sense probability distribution

9. Combining classifiers (cont.) determined:  Linear regression Minimize mean square error (MSE)  Expectation-Maximization (EM)  Approximate k with the performance of the classifier (PB)

10. Combining classifiers (cont.) 2. Combination based on Order Statistics

11. Combining classifiers (cont.) 3. Voting  (each classifier chose only one sense) Win the one with max. # of votes TagPair  Each classifier votes  Each pair of classifiers votes for the sense most likely by the joint classification Combining – stacking

12. Evaluation

13. Evaluation (unseen data)