Boosting applied to word sense disambiguation
In this paper we apply Schapire and Singer's AdaBoost.MH boosting algorithm to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar--based approaches, which represent state--o...
| Autores: | , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2000 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/93127 |
| Acceso en línea: | https://hdl.handle.net/2117/93127 |
| Access Level: | acceso abierto |
| Palabra clave: | AdaBoost.MH Word sense disambiguation WSD Polysemous words LazyBoosting Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | In this paper we apply Schapire and Singer's AdaBoost.MH boosting algorithm to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar--based approaches, which represent state--of--the--art accuracy on WSD. In order to make boosting practical for a real learning domain of thousands of words we study several ways of accelerating the algorithm by reducing the feature space. The best variant, which we call LazyBoosting, is tested on a medium--large sense--tagged corpus containing 192,800 examples of the 191 most frequent and ambiguous English words. Again, boosting compares favourably to the other benchmank algorithms. |
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