WordNet: a lexical database for English
Communications of the ACM
Foundations of statistical natural language processing
Foundations of statistical natural language processing
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Noun classification from predicate-argument structures
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Semantics Discovery via Human Computation Games
International Journal on Semantic Web & Information Systems
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In this paper, we face two problems in classical semantic similarity measures. Firstly, the context-dependency problem in knowledge-base measures since no one takes into account the context of the target domain. That is, a multisource context-dependent approach is presented. Secondly, the coverage problem with these measures since similarities can only be calculated between concepts included in a taxonomy. Moreover, "pure" corpus-based measures are still way from achieving performance reached by knowledge based measures. We present a more complex corpus-based approach using a taxonomy and data mining techniques in order to compute semantic distances between terms uncovered by the taxonomy. Experiments made show clearly the effectiveness of both proposed approaches.