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- W2548741423 abstract "Aviation safety research has placed an increasing emphasis on the use of data regarding safety problems to develop new processes to reduce risk. Large quantities of data are being made available and their use is becoming steadily more important. However, analysis of these data largely relies on manual effort. While effort has been made to structure these data, the tools available for using the natural language texts themselves are rudimentary. Designing additional tools for assisting research on these data is an important problem. As a step towards addressing this issue, this dissertation presents an investigation into applying data mining to extract related events from natural language texts on aviation safety. This research builds on techniques from data mining, natural language processing, and machine learning to identify events and discover event sequences. To minimize the need for domain specific knowledge engineering, the model and algorithms developed in this dissertation to address the problem use a minimum of outside knowledge. The model and algorithms function using merely raw text and a partial parser. The partial parser receives no domain specific information.To identify events in text, this work includes two components. The first is a model for identifying similar events in the raw texts. The second is a group of algorithms for identifying types of events that tend to occur together. These components work together, with the results of each component reinforcing the other. This dissertation presents a system incorporating the model and algorithms and presents results of several experiments conducted using the system. It presents a simulated inference scenario as a means of automatically testing the system's capabilities as well as manual testing and the results of an information retrieval experiment." @default.
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- W2548741423 date "2008-01-01" @default.
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- W2548741423 title "Mining events from aviation safety data" @default.
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