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Automatic Information Retrieval from Tweets: A Semantic Clustering Approach

Abstract : Much has been said about the value of social mediamessages for emergency services. The new uses related tothese platforms bring users to share information, otherwise unknown in crisis events. Thus, many studies have been performedin orderto identify tweets relating toa crisis event or to classify these tweets according to certain categories. However, determining the relevant information contained in the messages collected remains the responsibility of the emergency services. In this article, we introducethe issue of classifying the information contained in the messages. To do so, we use classes such as those usedby the operators in the call centers.Particularly we show that this problem is related to named entities recognition on tweets. We then explain that a semi-supervised approach might be beneficial,as the volume of data to perform this task is low.In a second part, we presentsome of the challenges raisedby this problematicand different ways to answer it.Finally, we explore one of them and its possible outcomes.
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Submitted on : Tuesday, September 1, 2020 - 10:24:50 AM
Last modification on : Friday, May 6, 2022 - 3:34:31 AM
Long-term archiving on: : Wednesday, December 2, 2020 - 1:12:52 PM


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  • HAL Id : hal-02926851, version 1



Julien Coche, Aurelie Montarnal, Andrea Tapia, Frederick Benaben. Automatic Information Retrieval from Tweets: A Semantic Clustering Approach. ISCRAM 2020 - 17th International conference on Information Systems for Crisis Response and Management, May 2020, Balcksburg, United States. p.134-141. ⟨hal-02926851⟩



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