Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Automatic discovery of synonyms and lexicalizations from the Web
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Automatic noun sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Two web-based approaches for noun sense disambiguation
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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Polysemy is one of the most difficult problems when dealing with natural language resources. Consequently, automated ontology learning from textual sources (such as web resources) is hampered by the inherent ambiguity of human language. In order to tackle this problem, this paper presents an automatic and unsupervised method for disambiguating taxonomies (the key component of a final ontology). It takes into consideration the amount of resources available in the Web as the base for inferring information distribution and semantics. It uses cooccurrence analysis and clustering techniques in order to group those taxonomical concepts that belong to the same “sense”. The final results are automatically evaluated against WordNet synsets.