Título: A maximum entropy-based word sense disambiguation system
Autores: Suárez Cueto, Armando
Palomar Sanz, Manuel
Fecha: 2007-07-18
2007-07-18
2002
2002
Publicador: RUA Docencia
Fuente:
Tipo: info:eu-repo/semantics/bookPart
Tema: Natural language processing
Semantics
Word sense disambiguation
Machine learning
Procesamiento del lenguaje natural
Semántica
Desambiguación del sentido de las palabras
Aprendizaje automático
Lenguajes y Sistemas Informáticos
Descripción: In this paper, a supervised learning system of word sense disambiguation is presented. It is based on conditional maximum entropy models. This system acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. Several types of features have been analyzed using the SENSEVAL-2 data for the Spanish lexical sample task. Such analysis shows that instead of training with the same kind of information for all words, each one is more effectively learned using a different set of features. This best-feature-selection is used to build some systems based on different maximum entropy classifiers, and a voting system helped by a knowledge-based method.
This paper has been partially supported by the Spanish Government (CICYT) under project number TIC2000-0664-C02-02.
Idioma: Inglés

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