Word sense disambiguation with pattern learning and automatic feature selection
Natural Language Engineering
Semantic tagging using WordNet examples
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Use of machine readable dictionaries for word-sense disambiguation in SENSEVAL-2
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Using domain information for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Pattern learning and active feature selection for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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
WASP-Bench: a lexicographic tool supporting word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Learning a robust word sense disambiguation model using hypernyms in definition sentences
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Word sense disambiguation using heterogeneous language resources
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Multiwords and word sense disambiguation
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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For SENSEVAL-2, we disambiguated the lexical sample using two different sense inventories. Official SENSEVAL-2 results were generated using WordNet, and separately using the New Oxford Dictionary of English (NODE). Since our initial submission, we have implemented additional routines and have now examined the differences in the features used for making sense selections. We report here the contribution of default sense selection, idiomatic usage, syntactic and semantic clues, subcategorization patterns, word forms, syntactic usage, context, selectional preferences, and topics or subject fields. We also compare the differences between WordNet and NODE. Finally, we compare these features to those identified as significant in supervised learning approaches.