In this paper, an approach to semantic disambiguation based on machine learning and semantic classes for Spanish is presented. A critical issue in a corpus-based approach for Word Sense Disambiguation (WSD) is the lack of wide-coverage resources to automatically learn the linguistic information. In particular, all-words sense annotated corpora such as SemCor do not have enough examples for many senses when used in a machine learning method. Using semantic classes instead of senses allows to collect a larger number of examples for each class while polysemy is reduced, improving the accuracy of semantic disambiguation. Cast3LB, a SemCor-like corpus, manually annotated with Spanish WordNet 1.5 senses, has been used in this paper to perform semantic disambiguation based on several sets of classes: lexicographer files of WordNet, WordNet Domains, and SUMO ontology.
CITATION STYLE
Izquierdo-Beviá, R., Moreno-Monteagudo, L., Navarro, B., & Suárez, A. (2006). SpringerLink - Capítulo del libro. (A. Gelbukh & C. A. Reyes-Garcia, Eds.), MICAI 2006: Advances in Artificial Intelligence (Vol. 4293, pp. 879–888). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/11925231
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