English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Event data, or structured records of ``who did what to whom'' that areautomatically extracted from text, is an important source of data for scholarsof international politics. The high cost of developing new event datasets,especially using automated systems that rely on hand-built dictionaries, meansthat most researchers draw on large, pre-existing datasets such as ICEWS ratherthan developing tailor-made event datasets optimized for their specificresearch question. This paper describes a ``bag of tricks'' for efficient,custom event data production, drawing on recent advances in natural languageprocessing (NLP) that allow researchers to rapidly produce customized eventdatasets. The paper introduces techniques for training an event categoryclassifier with active learning, identifying actors and the recipients ofactions in text using large language models and standard machine learningclassifiers and pretrained ``question-answering'' models from NLP, andresolving mentions of actors to their Wikipedia article to categorize them. Wedescribe how these techniques produced the new POLECAT global event datasetthat is intended to replace ICEWS, along with examples of how scholars canquickly produce smaller, custom event datasets. We publish example code andmodels to implement our new techniques.

Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks