3 code implementations • 3 Apr 2023 • Andrew Halterman, Philip A. Schrodt, Andreas Beger, Benjamin E. Bagozzi, Grace I. Scarborough
Event data, or structured records of ``who did what to whom'' that are automatically extracted from text, is an important source of data for scholars of international politics.
1 code implementation • 28 Mar 2023 • Andrew Halterman
Supervised text models are a valuable tool for political scientists but present several obstacles to their use, including the expense of hand-labeling documents, the difficulty of retrieving rare relevant documents for annotation, and copyright and privacy concerns involved in sharing annotated documents.
1 code implementation • 23 Mar 2023 • Andrew Halterman
Mordecai3 is a new end-to-end text geoparser and event geolocation system.
1 code implementation • Findings (ACL) 2021 • Andrew Halterman, Katherine A. Keith, Sheikh Muhammad Sarwar, Brendan O'Connor
Automated event extraction in social science applications often requires corpus-level evaluations: for example, aggregating text predictions across metadata and unbiased estimates of recall.
1 code implementation • Findings (ACL) 2021 • Andrew Halterman, Benjamin J. Radford
We propose a new task and dataset for a common problem in social science research: "upsampling" coarse document labels to fine-grained labels or spans.
1 code implementation • WS 2019 • Andrew Halterman
This work introduces a general method for automatically finding the locations where political events in text occurred.