2004 CotrainAndSelftrainForWSD

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Subject Headings: Word Sense Disambiguation Algorithm, Cotraining Algorithm, Self-Training Algorithm.


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This paper investigates the application of cotraining and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines cotraining with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.


This paper investigated the application of co-training and self-training to supervised word sense disambiguation. If the right parameters for co-training and self-training can be identified for each individual classifier, an average error reduction of 25.5% is achieved, with similar performance observed for both co-training and self-training. Given that these optimal settings cannot always be identified in practical applications, several algorithms for empirical parameter selection were investigated: global settings determined as the best set of parameters across all classifiers, and per-word settings, identified separately for each classifier, both using a validation set. An improved cotraining method was also introduced, that combines cotraining with majority voting, with the effect of smoothing the learning curves, and improving the average performance. This improved co-training algorithm, applied with a global parameter selection scheme, brought a significant error reduction of 9.8% with respect to the basic classifier, which shows that co-training can be successfully employed in practice for bootstrapping sense classifiers.


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2004 CotrainAndSelftrainForWSDRada MihalceaCo-training and Self-training for Word Sense DisambiguationProceedings of NAACL Conferencehttp://acl.ldc.upenn.edu/hlt-naacl2004/conll04/pdf/mihalcea.pdf2004