Abstract
In disasters, a large amount of information is exchanged via SMS messages. The content of these messages can be of high value and strategic interest. SMS messages tend to be informal and to contain abbreviations and misspellings, which are problems for current information extraction tools. Here, we describe an architecture designed to address the matter through four components: linguistic processing, temporal processing, event processing, and information fusion. Thereafter, we present a case study over a SMS corpus of messages sent to an electric utility company and a prototype built with Python and NLTK to validate the architecture’s information extraction components, obtaining Precision of 88%, Recall of 59% and F-measure (F1) of 71%. The work also serves as a roadmap to the treatment of emergency SMS in Portuguese.
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Monteiro, D., de Lima, V.L.S. (2015). A SMS Information Extraction Architecture to Face Emergency Situations. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_74
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DOI: https://doi.org/10.1007/978-3-319-23485-4_74
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