Boosting Applied toe Word Sense Disambiguation
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Specification Marks for Word Sense Disambiguation: New Development
CICLing '01 Proceedings of the Second International Conference on Computational Linguistics and Intelligent Text Processing
Word Sense Disambiguation with Specification Marks in Unrestricted Texts
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
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
Semantic pattern learning through maximum entropy-based WSD technique
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Combining knowledge- and corpus-based word-sense-disambiguation methods
Journal of Artificial Intelligence Research
Understanding the role of conceptual relations in Word Sense Disambiguation
Expert Systems with Applications: An International Journal
Building an optimal WSD ensemble using per-word selection of best system
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
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The WSD system presented at Senseval-2 uses a knowledge-based method for noun disambiguation and a corpus-based method for verbs and adjectives. The methods are, respectively, Specification Marks and Maximum Entropy probability models. So, we can say that this is a hybrid system which joins an unsupervised method with a supervised method. The whole system has been used in lexical sample english task and lexical sample spanish task.