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This presentation, led by Marian Olteanu and based on the work by Radu Florian and David Yarowsky, explores classifier ensemble methods to improve word sense disambiguation (WSD). The approach integrates multiple classifiers to minimize errors, especially on challenging examples. It covers various classifiers, including enhanced Naïve Bayes and mixture models, and outlines techniques for combining their outputs, such as voting and stacking. The evaluation of these methods on unseen data highlights their effectiveness in enhancing WSD accuracy.
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Modeling Consensus: Classifier Combination for WSD Authors: Radu Florian and David Yarowsky Presenter: Marian Olteanu
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
Approach & Features • Automatic POS tagging and lemma extraction • Features • Bag of words • Local • Syntactic
Classifier methods (6) • Vector-based • Enhanced Naïve Bayes • Weighted • Cosine • BayesRatio (good for sparse data)
Classifier methods (cont.) • MMVC (Mixture Maximum Variance Correction) • 2 stages • Second stage: select sense with variance over threshold
Classifier methods (cont.) • Discriminative Models • TBL (Transformation Based Learning) • Non-hierarchical decision lists
Combining classifiers • Agreement
Combining classifiers (cont.) • Three methods • Combine posterior sense probability distribution
Combining classifiers (cont.) • determined: • Linear regression • Minimize mean square error (MSE) • Expectation-Maximization (EM) • Approximate k with the performance of the classifier (PB)
Combining classifiers (cont.) • Combination based on Order Statistics
Combining classifiers (cont.) • 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
