Automated spatiotemporal and semantic information extraction for hazards

Abstract

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 occur over space and time. Chapter 3 presents an approach for retrieving semantic information about hazard events using ontologies and semantic gazetteers. With this work, information on the different kinds of events (e.g., impact, response, or recovery events) can be extracted as well as information about hazard events at different levels of detail. Using the methods presented in Chapter 2 and 3, an approach for automatically extracting spatial, temporal, and semantic information from tweets is discussed in Chapter 4. Four different elements of tweets are used for assigning appropriate spatial and temporal information to hazard events in tweets. Since tweets represent shorter, but more current information about hazards and how they are impacting a local area, key information about hazards can be retrieved through extracted spatiotemporal and semantic information from tweets.

An Ontology-Based Knowledge Representation Method for Typhoon Events from Chinese News Reports

“…By analyzing and referencing concepts and relationships of typhoon events from typhoon standard specifications, professional books, and the literature [46,50,52,70,71], this paper introduces a five-layer classification system for typhoon events based on the concepts, content, relationships and nature found in the news. The classification system results in the formation of an event category tree, illustrated in Figure 2.…”

Event Geoparser with Pseudo-Location Entity Identification and Numerical Extraction in Indonesian News Corpus

“…Enumerating all possible events semantic within a large corpus can be done by constructing semantic gazetteer, which is a list of keywords that can be used to represent concepts such as in [53]. This keyword, if obtained from large corpus will be able to increase the performance of the model on unseen data.…”

Extraction of spatio‐temporal data about historical events from text documents

“…Automated text analysis is used in many different applications, generally aiming at a reduction in the effort to analyze large amounts of texts. Application examples can be found in many areas for political texts (Grimmer & Stewart, ), Twitter messages as a supplement to public opinion polls (O'Connor, Balasubramanyan, Routledge, & Smith, ), biomedical literature (Cohen & Hersh, ), and news reports about natural hazards (Wang, ).…”

“…IE is the identification and extraction of the most relevant instances of a particular class of events or relationships in a natural language text and their transformation into a structured representation, such as a database (Feldman & Sanger, ; Campos et al, ). Researchers in GI‐Science have embraced IE techniques to explore geographical information from text documents and the capabilities to extract such information have improved in recent years (Wang, ). IE techniques provide an opportunity to automatically extract spatio‐temporal information from text documents through natural language processing (NLP) and text engineering methods.…”

“…Wang () used NLP concepts for the extraction of spatio‐temporal data and semantics extraction for natural hazards from web documents and articles. A similar but more extensive approach is proposed by Bekele () for the extraction of spatio‐temporal information from historical gazetteers.…”