Robust Semi-supervised and Ensemble-Based Methods in Word Sense Disambiguation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6233)


Mihalcea [1] discusses self-training and co-training in the context of word sense disambiguation and shows that parameter optimization on individual words was important to obtain good results. Using smoothed co-training of a naive Bayes classifier she obtains a 9.8% error reduction on Senseval-2 data with a fixed parameter setting. In this paper we test a semi-supervised learning algorithm with no parameters, namely tri-training [2]. We also test the random subspace method [3] for building committees out of stable learners. Both techniques lead to significant error reductions with different learning algorithms, but improvements do not accumulate. Our best error reduction is 7.4%, and our best absolute average over Senseval-2 data, though not directly comparable, is 12% higher than the results reported in Mihalcea [1].


Support Vector Machine Unlabeled Data Error Reduction Word Sense Disambiguation Random Subspace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Centre for Language TechnologyUniversity of CopenhagenCopenhagen S

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