<p>Event data provide high-resolution and high-volume information about political events. From COPDAB to KEDS, GDELT, ICEWS, and PHOENIX, event datasets and the frameworks that produce them have supported a variety of research efforts across fields and including political science. While these datasets are machine-coded from vast amounts of raw text input, they nonetheless require substantial human effort to produce and update sets of required dictionaries. I introduce a novel method for generating large dictionaries appropriate for event-coding given only a small sample dictionary. This technique leverages recent advances in natural language processing and deep learning to greatly reduce the researcher-hours required to go from defining a new domain-of-interest to producing structured event data that describes that domain. An application to cybersecurity is described and both the generated dictionaries and resultant event data are examined. The cybersecurity event data are also examined in relation to existing datasets in related domains.</p> / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/13386 |
| Date | January 2016 |
| Creators | Radford, Benjamin James |
| Contributors | Ward, Michael D |
| Source Sets | Duke University |
| Detected Language | English |
| Type | Dissertation |
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