Foundations of statistical natural language processing
Foundations of statistical natural language processing
Boosting Applied toe Word Sense Disambiguation
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
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
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In this paper, a supervised learning method of word sense disambiguation based on maximum entropy conditional probability models is presented. This system acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. Several types of features has been analyzed for a few words selected from the DSO corpus. The main contribution of this paper consists of the selection of the best sets of features for each word from the training data in order to build the classifiers. Our experimentation shows that our method reaches a good accuracy when it is compared with, for example, the systems at SENSEVAL-2.