An experiment in computational discrimination of English word senses
IBM Journal of Research and Development
A maximum entropy approach to natural language processing
Computational Linguistics
Two languages are more informative than one
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
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
Corpus-based statistical sense resolution
HLT '93 Proceedings of the workshop on Human Language Technology
HLT '93 Proceedings of the workshop on Human Language Technology
A new approach to word sense disambiguation
HLT '94 Proceedings of the workshop on Human Language Technology
Significant lexical relationships
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Selective sampling for example-based word sense disambiguation
Computational Linguistics
Decomposable modeling in natural language processing
Computational Linguistics
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
On Listing, Sampling, and Counting the Chordal Graphs with Edge Constraints
COCOON '08 Proceedings of the 14th annual international conference on Computing and Combinatorics
Determining the syntactic structure of medical terms in clinical notes
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
On listing, sampling, and counting the chordal graphs with edge constraints
Theoretical Computer Science
A new supervised learning algorithm for word sense disambiguation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Knowledge lean word sense disambiguation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Naive mixes for word sense disambiguation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Statistical models of word-sense disambiguation are often based on a small number of contextual features or on a model that is assumed to characterize the interactions among a set of features. Model selection is presented as an alternative to these approaches, where a sequential search of possible models is conducted in order to find the model that best characterizes the interactions among features. This paper expands existing model selection methodology and presents the first comparative study of model selection search strategies and evaluation criteria when applied to the problem of building probabilistic classifiers for word-sense disambiguation.