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An Automated Information Extraction Tool for International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design


Despite widespread recognition that aggregated summary statistics on international conflict and cooperation miss most of the complex interactions among nations, the vast majority of scholars continue to employ annual, quarterly, or (occasionally) monthly observations. Daily events data, coded from some of the huge volume of news stories produced by journalists, have not been used much for the past two decades. We offer some reason to change this practice, which we feel should lead to considerably increased use of these data. We address advances in event categorization schemes and software programs that automatically produce data by “reading” news stories without human coders. We design a method that makes it feasible, for the first time, to evaluate these programs when they are applied in areas with the particular characteristics of international conflict and cooperation data, namely event categories with highly unequal prevalences, and where rare events (such as highly conflictual actions) are of special interest. We use this rare events design to evaluate one existing program, and find it to be as good as trained human coders, but obviously far less expensive to use. For large-scale data collections, the program dominates human coding. Our new evaluative method should be of use in international relations, as well as more generally in the field of computational linguistics, for evaluating other automated information extraction tools. We believe that the data created by programs similar to the one we evaluated should see dramatically increased use in international relations research. To facilitate this process, we are releasing with this article data on 3.7 million international events, covering the entire world for the past decade.

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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

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Gary King , and Langche Zeng . 2001b. Logistic Regression in Rare Events Data. Political Analysis 9 (1):137–63.

Gary King , and Langche Zeng . 2002. Estimating Risk and Rate Levels, Ratios, and Differences in Case-Control Studies. Statistics in Medicine 21 (10):1409–27.

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Beth Sundheim . 1992. Overview of the Fourth Message Understanding Evaluation and Conference. In Proceedings of the Fourth Message Understanding Conference, edited by Beth Sundheim , 322. San Mateo, Calif.: Morgan Kaufmann.

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International Organization
  • ISSN: 0020-8183
  • EISSN: 1531-5088
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