A proposal of automatic selection of coarse-grained semantic classes for WSD

Abstract

Presentamos un método muy simple para seleccionar conceptos base (Base Level Concepts) usando algunas propiedades estructurales básicas de WordNet. Demostramos empíricamente que el conjunto de Base Level Concepts obtenido agrupa sentidos de palabras en un nivel de abstracción adecuado para la desambiguación del sentido de las palabras basada en clases. De hecho, un sencillo clasificador basado en el sentido más frecuente usando las clases generadas, es capaz de alcanzar un acierto próximo a 75% para la tarea de etiquetado semántico.We present a very simple method for selecting Base Level Concepts using some basic structural properties of WordNet. We also empirically demonstrate that these automatically derived set of Base Level Concepts group senses into an adequate level of abstraction in order to perform class-based Word Sense Disambiguation. In fact, a very naive Most Frequent classifier using the classes selected is able to perform a semantic tagging with accuracy figures over 75%.This paper has been supported by the European Union under the project QALL-ME (FP6 IST-033860) and the Spanish Government under the project Text-Mess (TIN2006-15265-C06-01) and KNOW (TIN2006-15049-C03-01