Using Machine Learning for Extracting Information from Natural Disaster News Reports
Usando Aprendizaje Automático para Extraer Información de Noticias de Desastres Naturales
Alberto Téllez Valero, Manuel Montes y Gómez and Luis Villaseñor Pineda
Laboratorio de Tecnologías del Lenguaje, Coordinación de Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE). Luis Enrique Erro #1, Tonantzintla, Puebla, México; albertotellezv@ccc.inaoep.mx , mmontesg@ccc.inaoep.mx , villasen@ccc.inaoep.mx
]]> Article received on July 17, 2008
Abstract
The disasters caused by natural phenomena have been present all along human history; nevertheless, their consequences are greater each time. This tendency will not be reverted in the coming years; on the contrary, it is expected that natural phenomena will increase in number and intensity due to the global warming. Because of this situation it is of great interest to have sufficient data related to natural disasters, since these data are absolutely necessary to analyze their impact as well as to establish links between their occurrence and their effects. In accordance to this necessity, in this paper we describe a system based on Machine Learning methods that improves the acquisition of natural disaster data. This system automatically populates a natural disaster database by extracting information from online news reports. In particular, it allows extracting information about five different types of natural disasters: hurricanes, earthquakes, forest fires, inundations, and droughts. Experimental results on a collection of Spanish news show the effectiveness of the proposed system for detecting relevant documents about natural disasters (reaching an F–measure of 98%), as well as for extracting relevant facts to be inserted into a given database (reaching an F–measure of 76%).
Keywords: Machine Learning, Information Extraction, Text Categorization, Natural Disasters, Databases.
Resumen.
Los desastres causados por fenómenos naturales han estado presentes desde el principio de la historia del hombre; sin embargo, sus consecuencias son cada vez mayores. Esta tendencia podría no ser revertida en los próximos años; al contrario, se espera que los fenómenos naturales puedan incrementar en número e intensidad debido al calentamiento global. A causa de esta situación es de gran interés tener suficientes datos relacionados a los desastres naturales, ya que estos datos son absolutamente necesarios para analizar su impacto así como para establecer conexiones entre su ocurrencia y sus efectos. En correspondencia con esta necesidad, en este artículo describimos un sistema basado en métodos de Aprendizaje Automático que mejora la adquisición de datos de desastres naturales. Este sistema automáticamente llena una base de datos de desastres naturales con la información extraída de noticias de periódicos en línea. En particular, este sistema permite extraer información acerca de cinco tipos de desastres naturales: huracanes, temblores, incendios forestales, inundaciones y sequías. Los resultados experimentales en una colección de noticias en Español muestran la eficacia del sistema propuesto tanto para detectar documentos relevantes sobre desastres naturales (alcanzando una medida–F de 98%), así como para extraer hechos relevantes para ser insertados en una base de datos dada (alcanzando una medida–F de 76%).
Palabras claves: Aprendizaje Automático, Extracción de Información, Clasificación Temática de Textos, Desastres Naturales, Bases de Datos.
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Acknowledgments
This work was partially supported by Conacyt through research grants (CB–61335, CB–82050 and CB–83459) and scholarship (171610).
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