| Título: | Using Machine Learning for Extracting Information from Natural Disaster News Reports |
| Autores: |
Téllez Valero,Alberto Montes y Gómez,Manuel Villaseñor Pineda,Luis |
| Fecha: | 2009-09-01 |
| Publicador: | SCIELO |
| Fuente: |
 |
| Tipo: | journal article |
| Tema: |
Machine Learning Information Extraction Text Categorization Natural Disasters Databases |
| Descripción: | 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%). |
| Idioma: | Inglés |
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