Exploiting Geolocation, User and Temporal Information for Natural Hazards Monitoring in Twitter

  1. Víctor Fresno
  2. Arkaitz Zubiaga
  3. Heng Ji
  4. Raquel Martínez
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2015

Número: 54

Páginas: 85-92

Tipo: Artículo

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Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

Cuando se producen eventos relacionados con situaciones de emergencia, es importante acceder a tanta información como sea posible relacionada con dicho evento. En este contexto algunas redes sociales como Twitter suponen un importante recurso de información en tiempo real. La técnicas clásicas de filtrado de información suelen centrarse en el análisis de coocurrencia de términos con el conjunto de palabras clave inicialmente consideradas. Sin embargo, estas aproximaciones pueden perder información, ya que no son capaces de recuperar información relevante que venga expresada con palabras que no coocurran con las palabras clave inicialmente usadas, y que expresan nuestra necesidad de información. Considerar información de geolocalización, usuario o temporal dentro de un enfoque de pseudo-relevance feedback, nos permite encontrar terminología relacionada con el evento, pero no coocurrente con las palabras clave inicialmente consideradas. Por otro lado, considerando el aspecto temporal se puede modificar una función de expansión de consultas como la divergencia de Kullback-Leibler con el fin de mejorar el filtrado de información en estas situaciones de emergencia. Nuestras propuestas se han evaluado en dos colecciones de eventos del mundo real obteniéndose resultados alentadores.

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Información de financiación

Financiadores

  • U.S. Army Research Laboratory
    • W911NF-09-2-0053
  • Norfolk Southern

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