An Unsupervised Algorithm for Person Name Disambiguation in the Web

• Autores: Agustín D. Delgado, Raquel Martínez Unanue , Soto Montalvo Herranz , Víctor Fresno Fernández
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 53, 2014, págs. 51-58
• Idioma: inglés
• Títulos paralelos:
  • Algoritmo no Supervisado para Desambiguacion de Nombres de Personas en la Web
• Enlaces
  • Texto completo
• Dialnet Métricas: 2 Citas
• Resumen
  • español

    En este trabajo presentamos un sistema no supervisado para agrupar los resultados proporcionados por un motor de búsqueda cuando la consulta corresponde a un nombre de persona compartido por diferentes individuos. Las páginas web se representan mediante n-gramas de diferente información y tamaño. Además, proponemos un algoritmo de clustering capaz de calcular el número de clusters y devolver grupos de páginas web correspondientes a cada uno de los individuos, sin necesidad de entrenamiento ni umbrales predefinidos, como hacen los mejores sistemas del estado del arte en esta tarea. Hemos evaluado nuestra propuesta con tres colecciones de evaluación propuestas en diferentes campañas de evaluación para la tarea de Desambiguación de Personas en la Web. Los resultados obtenidos son competitivos y comparables a aquellos obtenidos por los mejores sistemas del estado del arte que utilizan algún tipo de supervisión.

  • English

    In this paper we present an unsupervised approach for clustering the results of a search engine when the query is a person name shared by different individuals. We represent the web pages using n-grams, comparing different kind of information and different length of n-grams. Moreover, we propose a new clustering algorithm that calculates the number of clusters and establishes the groups of web pages according to the different individuals, without the need of any training data or predefined thresholds, as the successful state of the art systems do. Our approach is compared with three gold standard collections compiled by different evaluation campaigns for the task of Web People Search. We obtain really competitive results, comparable to those obtained by the best approaches that use annotated data.

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