Modeling Consensus: Classifier Combination for WSD

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

Evaluation

Evaluation (unseen data)

Modeling Consensus: Classifier Combination for WSD