Automatic retrieval of actionable information from disaster-related microblogs
This paper discusses our work submitted to FIRE 2017 IRMiDis Track . The goal was to extract actionable information from the micro-blogs i.e. tweets which can be leveraged to provide aid and help during disaster events. The two tasks addressed in this work are, first, extraction of useful information such as the need or availability of various resources and second, finding tweets that express the need and availability of the same resources. Our approach is based on leveraging a mix of linguistics and machine learning techniques. The evaluation scores of the submitted runs are reported in terms of Precision@100, Recall@100 and MAP. The average MAP score is reported to be 0.1304 for the identification of need and availability tweets. The score for the matching task is reported in terms of the F-score which came out to be 0.2424.