Portfolio item number 1
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Short description of portfolio item number 2
(with Sebastian Schutte and Michael Ward)
Working Paper
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(with Christian Davenport and Christopher M. Sullivan)
Working Paper
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(with Juan Tellez)
Under Review
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(with S. Minhas, C. Dorff, M. Foster, M. Gallop, J. Tellez and M. Ward)
Under Review
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Stuart A. Bremer Award from the Peace Science Society (2018)
Under Review
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(with Kyle Beardsley, Peter Mucha, David Siegel, and Juan Tellez)
Journal of Politics (forthcoming)
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(with Sophie Lee and Michael Ward)
Political Science Research and Methods (2018)
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Published in UC San Francisco, Department of Testing, 2012
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Published in UC-Berkeley Institute for Testing Science, 2013
Published in London School of Testing, 2014
Published in Presented at Polmeth 2017, 2017
Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training dataset and use this model to predict whether a location word in the test dataset accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results of an evaluation show that the proposed algorithm out-performs existing geocoders in terms of accuracy. Download paper here
Undergraduate course, Teaching Assistant
Instructor: Kyle Beardsley
Undergraduate course, Teaching Assistant
Instructor: David Siegel
Undergraduate course, Section Lecturer
Instructor: Peter Feaver
[Teaching Evaluation]