Machine Learning
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
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Relieving the data acquisition bottleneck in word sense disambiguation
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
SemEval-2007 task 01: evaluating WSD on cross-language information retrieval
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UNIBA: JIGSAW algorithm for word sense disambiguation
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Combining learning and word sense disambiguation for intelligent user profiling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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This paper presents a WSD strategy which combines a knowledge-based method that exploits sense definitions in a dictionary and relations among senses in a semantic network, with supervised learning methods on annotated corpora. The idea behind the approach is that the knowledge-based method can cope with the possible lack of training data, while supervised learning can improve the precision of a knowledge-based method when training data are available. This makes the proposed method suitable for disambiguation of languages for which the available resources are lacking in training data or sense definitions. In order to evaluate the effectiveness of the proposed approach, experimental sessions were carried out on the dataset used for the WSD task in the EVALITA 2007 initiative, devoted to the evaluation of Natural Language Processing tools for Italian. The most effective hybrid WSD strategy is the one that integrates the knowledge-based approach into the supervised learning method, which outperforms both methods taken singularly.