Foundations of statistical natural language processing
Foundations of statistical natural language processing
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Boosting Applied toe Word Sense Disambiguation
ECML '00 Proceedings of the 11th European Conference on Machine Learning
A Baseline Methodology for Word Sense Disambiguation
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Feature Selection Analysis for Maximum Entropy-Based WSD
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
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
Refined lexicon models for statistical machine translation using a maximum entropy approach
ACL '01 Proceedings of the 39th 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
Mapping WordNets using structural information
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Experiments in word domain disambiguation for parallel texts
WorkSense '00 Proceedings of the ACL-2000 Workshop on Word Senses and Multi-Linguality
Using domain information for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Word domain disambiguation via word sense disambiguation
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
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In this paper, a supervised learning system of word sense disambiguation is presented. It is based on conditional maximum entropy models. This system acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. The system were evaluated both using WordNet's senses and domains as the sets of classes of each word. Domain labels are obtained from the enrichment of WordNet with subject field codes which produces a polysemy reduction. Several types of features has been analyzed for a few words selected from the DSO corpus. Using the domain enrichment of WordNet, a 7% of accuracy improvement is achieved.