Automated spatiotemporal and semantic information extraction for hazards

Automated spatiotemporal and semantic information extraction for hazards." PhD (Doctor of Philosophy) thesis, ii To everyone who has supported and helped me over the years. iii ACKNOWLEDGMENTS It has been a long journey since I started my Ph.D. study at the University of Iowa. During the six years, I received many supports from my adviser, professors, my family and friends. First of all, I would like to give my sincerest thanks to my adviser Dr. Kathleen Stewart for her enormous contributions of advices and time. She brought me to this research field to start the journey. She gave me a lot of inspirations to explore the new path, and kept me on the right track. I also want to thank my committee members Dr. time and supports. Thanks to all professors and colleagues in the Department of Geographical and Sustainability Science at University of Iowa for their help during my study period. I would like to thank my family and friends for all their supports and love during the 6 years for my Ph.D. study. Last but not least, I want to give my best thanks to my husband. He gives me a lot of supports and encouragement throughout my study. Without his supports, I could not achieve to the destination of the journey. Thank you to all of you. iv ABSTRACT This dissertation explores three research topics related to automated spatiotemporal and semantic information extraction about hazard events from Web news reports and other social media. The dissertation makes a unique contribution of bridging geographic information science, geographic information retrieval, and natural language processing. Geographic information retrieval and natural language processing techniques are applied to extract spatiotemporal and semantic information automatically from Web documents, to retrieve information about patterns of hazard events that are not explicitly described in the texts. Chapters 2, 3 and 4 can be regarded as three standalone journal papers. The research topics covered by the three chapters are related to each other, and are presented in a sequential way. Chapter 2 begins with an investigation of methods for automatically extracting spatial and temporal information about hazards from Web news reports. A set of rules is developed to combine the spatial and temporal information contained in the reports based on how this information is presented in text in order to capture the dynamics of hazard events (e.g., changes in event locations, new events occurring) as they …

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