On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to the special issue on word sense disambiguation: the state of the art
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
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Word-sense disambiguation using decomposable models
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Combining heterogeneous classifiers for word-sense disambiguation
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Trajectory based word sense disambiguation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Data & Knowledge Engineering
Semi-supervised learning integrated with classifier combination for word sense disambiguation
Computer Speech and Language
Investigating problems of semi-supervised learning for word sense disambiguation
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
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This paper proposes a framework for combining classifiers based on OWA operators in which each individual classifier uses a distinct representation of objects to be classified. It is shown that this framework yields several commonly used decision rules but without some strong assumptions made in the work by Kittler et al. [7]. As an application, we apply the proposed framework of classifier combination to the problem of word sense disambiguation (shortly, WSD). To this end, we experimentally design a set of individual classifiers, each of which corresponds to a distinct representation type of context considered in the WSD literature, and then the proposed combination strategies are experimentally tested on the datasets for four polysemous words, namely interest, line, serve, and hard, and compared to previous studies.