# Scale Up Event Extraction Learning via Automatic Training Data Generation

arXiv: https://arxiv.org/abs/1712.03665 · HTML: https://mlsanity.com/paper/1712.03665/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2017.

## Built on by (most cited first, 11 of 11)

- [Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker](https://mlsanity.com/paper/2105.14924/) (2021)
- [Joint Multimedia Event Extraction from Video and Article](https://mlsanity.com/paper/2109.12776/) (2021)
- [GLEN: General-Purpose Event Detection for Thousands of Types](https://mlsanity.com/paper/2303.09093/) (2023)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations](https://mlsanity.com/paper/2106.12384/) (2021)
- [Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?](https://mlsanity.com/paper/2307.07047/) (2023)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Event Extraction: A Survey](https://mlsanity.com/paper/2210.03419/) (2022)
- [Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction](https://mlsanity.com/paper/2503.02628/) (2025)
- [Trigger-free Event Detection via Derangement Reading Comprehension](https://mlsanity.com/paper/2208.09659/) (2022)
- [Judicial Intelligent Assistant System: Extracting Events from Divorce Cases to Detect Disputes for the Judge](https://mlsanity.com/paper/2303.16751/) (2023)

## Abstract (arXiv)

The task of event extraction has long been investigated in a supervised learning paradigm, which is bound by the number and the quality of the training instances. Existing training data must be manually generated through a combination of expert domain knowledge and extensive human involvement. However, due to drastic efforts required in annotating text, the resultant datasets are usually small, which severally affects the quality of the learned model, making it hard to generalize. Our work develops an automatic approach for generating training data for event extraction. Our approach allows us to scale up event extraction training instances from thousands to hundreds of thousands, and it does this at a much lower cost than a manual approach. We achieve this by employing distant supervision to automatically create event annotations from unlabelled text using existing structured knowledge bases or tables.We then develop a neural network model with post inference to transfer the knowledge extracted from structured knowledge bases to automatically annotate typed events with corresponding arguments in text.We evaluate our approach by using the knowledge extracted from Freebase to label texts from Wikipedia articles. Experimental results show that our approach can generate a large number of high quality training instances. We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.