Building a large-scale knowledge base for machine translation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
WSD Algorithm Applied to a NLP System
NLDB '00 Proceedings of the 5th International Conference on Applications of Natural Language to Information Systems-Revised Papers
Word Sense Disambiguation with Specification Marks in Unrestricted Texts
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Topical clustering of MRD senses based on information retrieval techniques
Computational Linguistics - Special issue on word sense disambiguation
TGE: Tlinks Generation Environment
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Mapping WordNets using structural information
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Building a large ontology for machine translation
HLT '93 Proceedings of the workshop on Human Language Technology
Building Japanese-English dictionary based on ontology for machine translation
HLT '94 Proceedings of the workshop on Human Language Technology
Experiments in word domain disambiguation for parallel texts
WorkSense '00 Proceedings of the ACL-2000 Workshop on Word Senses and Multi-Linguality
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This paper presents a new method to enrich semantically WordNet with categories from general domain classification systems. The method is performed in two consecutive steps. First, a lexical knowledge word sense disambiguation process. Second, a set of rules to select the main concepts as representatives for each category. The method has been applied to label automatically WordNet synsets with Subject Codes from a standard news agencies classification system. Experimental results show than the proposed method achieves more than 95% accuracy selecting the main concepts for each category.