C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Neural Networks
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
HLT '91 Proceedings of the workshop on Speech and Natural Language
HLT '93 Proceedings of the workshop on Human Language Technology
Resolving abbreviations to their senses in Medline
Bioinformatics
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Cuisine: Classification using stylistic feature sets and-or name-based feature sets
Journal of the American Society for Information Science and Technology
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Abbreviations are very common and are widely used in both written and spoken language. However, they are not always explicitly defined and in many cases they are ambiguous. In this research, we present a process that attempts to solve the problem of abbreviation ambiguity. Various features have been explored, including context-related methods and statistical methods. The application domain is Jewish Law documents written in Hebrew, which are known to be rich in ambiguous abbreviations. Various variants of the one sense per discourse hypothesis (by varying the scope of discourse) have been implemented. Several common machine learning methods have been tested to find a successful integration of these variants. The best results have been achieved by SVM, with 96.09% accuracy.