Using multiple knowledge sources for word sense discrimination
Computational Linguistics
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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
A new supervised learning algorithm for word sense disambiguation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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
Improving Feature Selection for Maximum Entropy-Based Word Sense Disambiguation
PorTAL '02 Proceedings of the Third International Conference on Advances in Natural Language Processing
Word Sense vs. Word Domain Disambiguation: A Maximum Entropy Approach
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
A maximum entropy-based word sense disambiguation system
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Word Sense Disambiguation Using Inductive Logic Programming
Inductive Logic Programming
Tool for computer-aided Spanish word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
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This paper describes a methodology for supervised word sense disambiguation that relies on standard machine learning algorithms to induce classifiers from sense-tagged training examples where the context in which ambiguous words occur are represented by simple lexical features. This constitutes a baseline approach since it produces classifiers based on easy to identify features that result in accurate disambiguation across a variety of languages. This paper reviews several systems based on this methodology that participated in the Spanish and English lexical sample tasks of the SENSEVAL-2 comparative exercise among word sense disambiguation systems. These systems fared much better than standard baselines, and were within seven to ten percentage points of accuracy of the mostly highly ranked systems.