Spatio-Temporal Analysis on FEMA Situation Updates with Automated Information Extraction
Chi-Chun Pan, Prasenjit Mitra, Auroop Ratan Ganguly · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2007
With the advent of the World-Wide-Web, there is an over-abundance of textual information. Information present in digital documents can be utilized better if it can be extracted automatically and scalably from text and visualized using visualization tools. In this paper, we present an automated information extraction and visualization tool for human sensor data. Our system consists of three main components: FactXtractor, GeoTagger, and FEMARepViz. Named entities and entity relations are extracted using FactXtractor. We have proposed a novel stripped dependency tree kernel for a Support Vector Machine (SVM) based classifier to identify semantic relationships among entities. GeoTagger disambiguates location entities. Built on top of the first two system components, FemaRepViz is an application that segments text documents, identifies the topic of the segments, extracts location entities, disambiguates them, and visualizes the extracted information on Google Earth or Google Map. Our empirical evaluation shows that the system achieves reasonable accuracy.