Optimizing Planar 2-Planar Parsers with MaltOptimizer

• Autores: Miguel Ballesteros, Carlos Gómez Rodríguez , Joakim Nivre
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 49, 2012, págs. 171-178
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen
  • español

    MaltOptimizer es una herramienta capaz de proporcionar una optimizaci on para modelos generados mediante MaltParser. Los analizadores de dependencias actuales requieren una completa con guracion para obtener resultados a la altura del estado del arte, y para ello es necesario un conocimiento especializado.

    Los analizadores Planar y 2-Planar son dos algoritmos diferentes y de reciente incorporaci on en MaltParser. En el presente artculo presentamos como estos dos analizadores pueden incluirse en MaltOptimizer comparandolos con el resto de familias de algoritmos incluidas en MaltParser, y como se puede de nir una busqueda y selecci on de atributos (o \features") usando el propio sistema para estos dos parsers.

    Los experimentos muestran que usando estos metodos podemos mejorar la precision obtenida hasta un porcentaje absoluto del 8 por ciento (labeled attachment score) si lo comparamos con una con guracion basica de estos 2 parsers.

  • English

    MaltOptimizer is a tool that is capable of nding an optimal con guration for MaltParser models, taking into account that nowadays dependency parsers require careful tuning in order to obtain state-of-the-art results, and this tuning is normally based on specialized knowledge. The Planar and 2-Planar parsers are two di erent parsing algorithms included in MaltParser. In the present paper, we show how these two parsers can be included in MaltOptimizer processes comparing them with the rest of MaltParser algorithm families, and how we can de ne a deep feature search and selection by using MaltOptimizer for these two algorithms. The experiments show that by using MaltOptimizer we can improve parsing accuracy for Planar and 2-Planar parsers by up to 8 percent absolute (labeled attachment score) compared to default settings.

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