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Modeling consensus: classifier combination for word sense disambiguation (2002).
| Content Provider | CiteSeerX |
|---|---|
| Author | Yarowsky, David Florian, Radu |
| Abstract | This paper demonstrates the substantial empirical success of classifier combination for the word sense disambiguation task. It investigates more than 10 classifier combination methods, including second order classifier stacking, over 6 major structurally different base classifiers (enhanced Nave Bayes, cosine, Bayes Ratio, decision lists, transformationbased learning and maximum variance boosted mixture models). The paper also includes in-depth performance analysis sensitive to properties of the feature space and component classifiers. When evaluated on the standard SENSEVAL1 and 2 data sets on 4 languages (English, Spanish, Basque, and Swedish), classifier combination performance exceeds the best published results on these data sets. 1 |
| File Format | |
| Publisher Date | 2002-01-01 |
| Access Restriction | Open |
| Subject Keyword | Feature Space Bayes Ratio Maximum Variance Second Order Classifier Stacking Data Set Substantial Empirical Success In-depth Performance Analysis Standard Senseval1 Classifier Combination Word Sense Disambiguation Task Combination Method Different Base Classifier Nave Bayes Mixture Model Combination Performance Word Sense Disambiguation Component Classifier Decision List |
| Content Type | Text |
| Resource Type | Article |
