Word sense disambiguation using a second language monolingual corpus
Computational Linguistics
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
One-class svms for document classification
The Journal of Machine Learning Research
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
Word sense disambiguation using label propagation based semi-supervised learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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Supervised and semi-supervised sense disambiguation methods will mis-tag the instances of a target word if the senses of these instances are not defined in sense inventories or there are no tagged instances for these senses in training data. Here we used a model order identification method to avoid the misclassification of the instances with undefined senses by discovering new senses from mixed data (tagged and untagged corpora). This algorithm tries to obtain a natural partition of the mixed data by maximizing a stability criterion defined on the classification result from an extended label propagation algorithm over all the possible values of the number of senses (or sense number, model order). Experimental results on SENSEVAL-3 data indicate that it outperforms SVM, a one-class partially supervised classification algorithm, and a clustering based model order identification algorithm when the tagged data is incomplete.