Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
A divide-and-merge methodology for clustering
ACM Transactions on Database Systems (TODS)
Ensemble methods for unsupervised WSD
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automatic cluster stopping with criterion functions and the gap statistic
NAACL-Demonstrations '06 Proceedings of the 2006 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume: demonstrations
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Semeval-2007 task 02: evaluating word sense induction and discrimination systems
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UBC-AS: a graph based unsupervised system for induction and classification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Evaluating topic models for digital libraries
Proceedings of the 10th annual joint conference on Digital libraries
SemEval-2010 task 14: Word sense induction & disambiguation
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Word sense induction & disambiguation using hierarchical random graphs
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Latent semantic word sense induction and disambiguation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Optimizing semantic coherence in topic models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Ensembles combine knowledge from distinct machine learning approaches into a general flexible system. While supervised ensembles frequently show great benefit, unsupervised ensembles prove to be more challenging. We propose evaluating various unsupervised ensembles when applied to the unsupervised task of Word Sense Induction with a framework for combining diverse feature spaces and clustering algorithms. We evaluate our system using standard shared tasks and also introduce new automated semantic evaluations and supervised baselines, both of which highlight the current limitations of existing Word Sense Induction evaluations.