WordNet: a lexical database for English
Communications of the ACM
Knowledge lean word-sense disambiguation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Word-sense disambiguation using decomposable models
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
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
Corpus-based statistical sense resolution
HLT '93 Proceedings of the workshop on Human Language Technology
WordNet Nouns: Classes and Instances
Computational Linguistics
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Adjective Sense Disambiguation at the Border Between Unsupervised and Knowledge-Based Techniques
Fundamenta Informaticae
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
State of the art versus classical clustering for unsupervised word sense disambiguation
Artificial Intelligence Review
Fundamenta Informaticae - Emergent Computing
Unsupervised word sense disambiguation with N-gram features
Artificial Intelligence Review
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This paper aims to fully present a new word sense disambiguation method that has been introduced in Hristea and Popescu (Fundam Inform 91(3---4):547---562, 2009) and so far tested in the case of adjectives (Hristea and Popescu in Fundam Inform 91(3---4):547---562, 2009) and verbs (Hristea in Int Rev Comput Softw 4(1):58---67, 2009). We hereby extend the method to the case of nouns and draw conclusions regarding its performance with respect to all these parts of speech. The method lies at the border between unsupervised and knowledge-based techniques. It performs unsupervised word sense disambiguation based on an underlying Naïve Bayes model, while using WordNet as knowledge source for feature selection. The performance of the method is compared to that of previous approaches that rely on completely different feature sets. Test results for all involved parts of speech show that feature selection using a knowledge source of type WordNet is more effective in disambiguation than local type features (like part-of-speech tags) are.