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

arXiv: https://arxiv.org/abs/2304.01331 · HTML: https://mlsanity.com/paper/2304.01331/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [Large-Scale QA-SRL Parsing](https://mlsanity.com/paper/1805.05377/) (2018)
- [Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence](https://mlsanity.com/paper/2105.12936/) (2021)
- [Synthetically generated text for supervised text analysis](https://mlsanity.com/paper/2303.16028/) (2023)
- [PoMo: Generating Entity-Specific Post-Modifiers in Context](https://mlsanity.com/paper/1904.03111/) (2019)
- [Geolocating Political Events in Text](https://mlsanity.com/paper/1905.12713/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Multi-solution Study on GDPR AI-enabled Completeness Checking of DPAs](https://mlsanity.com/paper/2311.13881/) (2023)
- [Toward Better Temporal Structures for Geopolitical Events Forecasting](https://mlsanity.com/paper/2601.00430/) (2026)

## Abstract (arXiv)

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.