# Rapid Customization for Event Extraction

arXiv: https://arxiv.org/abs/1809.07783 · HTML: https://mlsanity.com/paper/1809.07783/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19](https://mlsanity.com/paper/2105.01819/) (2021)

## Abstract (arXiv)

We present a system for rapidly customizing event extraction capability to find new event types and their arguments. The system allows a user to find, expand and filter event triggers for a new event type by exploring an unannotated corpus. The system will then automatically generate mention-level event annotation automatically, and train a Neural Network model for finding the corresponding event. Additionally, the system uses the ACE corpus to train an argument model for extracting Actor, Place, and Time arguments for any event types, including ones not seen in its training data. Experiments show that with less than 10 minutes of human effort per event type, the system achieves good performance for 67 novel event types. The code, documentation, and a demonstration video will be released as open source on github.com.