摘要
The natural language processing has a set of phases that evolves from lexical text analysis to the pragmatic one in which the author’s intentions are shown. The ambiguity problem appears in all of these tasks. Previous works tries to do word sense disambiguation, the process of assign a sense to a word inside a specific context, creating algorithms under a supervised or unsupervised approach, which means that those algorithms use or not an external lexical resource. This paper presents an approximated approach that combines not supervised algorithms by the use of a classifiers set, the result will be a learning algorithm based on unsupervised methods for word sense disambiguation process. It begins with an introduction to word sense disambiguation concepts and then analyzes some unsupervised algorithms in order to extract the best of them, and combines them under a supervised approach making use of some classifiers.
The natural language processing has a set of phases that evolves from lexical text analysis to the pragmatic one in which the author’s intentions are shown. The ambiguity problem appears in all of these tasks. Previous works tries to do word sense disambiguation, the process of assign a sense to a word inside a specific context, creating algorithms under a supervised or unsupervised approach, which means that those algorithms use or not an external lexical resource. This paper presents an approximated approach that combines not supervised algorithms by the use of a classifiers set, the result will be a learning algorithm based on unsupervised methods for word sense disambiguation process. It begins with an introduction to word sense disambiguation concepts and then analyzes some unsupervised algorithms in order to extract the best of them, and combines them under a supervised approach making use of some classifiers.
