  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. The high cost of developing new event datasets,
especially using automated systems that rely on hand-built dictionaries, means
that most researchers draw on large, pre-existing datasets such as ICEWS rather
than developing tailor-made event datasets optimized for their specific
research question. This paper describes a ``bag of tricks'' for efficient,
custom event data production, drawing on recent advances in natural language
processing (NLP) that allow researchers to rapidly produce customized event
datasets. The paper introduces techniques for training an event category
classifier with active learning, identifying actors and the recipients of
actions in text using large language models and standard machine learning
classifiers and pretrained ``question-answering'' models from NLP, and
resolving mentions of actors to their Wikipedia article to categorize them. We
describe how these techniques produced the new POLECAT global event dataset
that is intended to replace ICEWS, along with examples of how scholars can
quickly produce smaller, custom event datasets. We publish example code and
models to implement our new techniques.

Computation and Language