Modeling Consensus: Classifier Combination for WSD

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Modeling Consensus Classifier Combination for WSD

• Authors Radu Florian and David Yarowsky
• Presenter Marian Olteanu

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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

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Approach Features

• Automatic POS tagging and lemma extraction
• Features
• Bag of words
• Local
• Syntactic

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Classifier methods (6)

• Vector-based
• Enhanced Naïve Bayes
• Weighted
• Cosine
• BayesRatio (good for sparse data)

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Classifier methods (cont.)

• MMVC (Mixture Maximum Variance Correction)
• 2 stages
• Second stage select sense with variance over
  threshold

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Classifier methods (cont.)

• Discriminative Models
• TBL (Transformation Based Learning)
• Non-hierarchical decision lists

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Combining classifiers

• Agreement

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Combining classifiers (cont.)

• Three methods
• Combine posterior sense probability distribution

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Combining classifiers (cont.)

• ? determined
• Linear regression
• Minimize mean square error (MSE)
• Expectation-Maximization (EM)
• Approximate ?k with the performance of the
  classifier (PB)

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Combining classifiers (cont.)

1. Combination based on Order Statistics

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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

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Evaluation
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Evaluation (unseen data)