Um Framework de Extração e Etiquetamento de Informações de Trânsito
Abstract
With the great development of computational technologies, it has been possible to use social networks to collect and analyze information of individuals, communities and with respect to cities in real time. The information overload, however, is a challenge even in the context of cities, where many events occur in parallel. In this context, this paper describes the development of a framework that seeks to ease the extraction, treatment and identification of events and their locations in tweets written in the Portuguese language using the Conditional Random Fields technique to address the Named Entity Recognition task. The obtained results demonstrate the potential of the tool in identifying important traffic events in a given location of interest.
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