Boosting Applied toe Word Sense Disambiguation
ECML '00 Proceedings of the 11th 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
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Engineering Applications of Artificial Intelligence
Learning semantic classes for word sense disambiguation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Automatic identification of infrequent word senses
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Scaling up word sense disambiguation via parallel texts
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
NUS-PT: exploiting parallel texts for word sense disambiguation in the English all-words tasks
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Class-based approach to disambiguating levin verbs
Natural Language Engineering
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We report on the use of machine learning techniques for word sense disambiguation in the English all words task of SENSEVAL2. The task was to automatically assign the appropriate sense to a possibly ambiguous word form given its context. A "word expert" approach was adopted, leading to a set of classifiers, each specialized in one single word form-POS combination. Experts consist of multiple classifiers trained on Semcor using two types of learning techniques, viz. memory-based learning and rule-induction. Through optimization by crossvalidation of the individual classifiers and the voting scheme for combining them, the best possible word expert was determined. Results show that especially memory-based learning in a word-expert approach is a feasible method for unrestricted word-sense disambiguation, even with limited training data.