Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
MICAI '06 Proceedings of the Fifth Mexican International Conference on Artificial Intelligence
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Experiments in word domain disambiguation for parallel texts
WorkSense '00 Proceedings of the ACL-2000 Workshop on Word Senses and Multi-Linguality
Defining classifier regions for WSD ensembles using word space features
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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In Natural Language Processing there are different problems to solve: lexical ambiguity, summarization, information extraction, speech processing, etc. In particular, lexical ambiguity is a difficult task that nowadays is still open to new approaches. In fact, there is still a lack of systems that solve efficiently this kind of problem. At present, we find two different approaches: knowledge systems and machine learning systems. Recent studies demonstrate that machine learning systems obtain better results than knowledge systems but there is a problem: the lack of annotated contexts and corpus to train the systems. In this work, we try to avoid this situation by combining a new machine learning system with a knowledge based system.