On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability
ECML '93 Proceedings of the European Conference on Machine Learning
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
HLT '93 Proceedings of the workshop on Human Language Technology
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
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
Sentiment tagging of adjectives at the meaning level
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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This article describes the results of a systematic in-depth study of the criteria used for word sense disambiguation. Our study is based on 60 target words: 20 nouns, 20 adjectives and 20 verbs. Our results are not always in line with some practices in the field. For example, we show that omitting non-content words decreases performance and that bigrams yield better results than unigrams.