Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
A Baseline Methodology for Word Sense Disambiguation
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
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
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
A decision tree of bigrams is an accurate predictor of word sense
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Machine learning with lexical features: the Duluth approach to Senseval-2
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Classification Approach to Word Selection in Machine Translation
AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
Making sense of word sense variation
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Anveshan: a framework for analysis of multiple annotators' labeling behavior
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
Multiplicity and word sense: evaluating and learning from multiply labeled word sense annotations
Language Resources and Evaluation
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This paper presents an evaluation of an ensemble-based system that participated in the English and Spanish lexical sample tasks of SENSEVAL-2. The system combines decision trees of unigrams, bigrams, and co---occurrences into a single classifier. The analysis is extended to include the SENSEVAL-1 data.