# Event Detection with Neural Networks: A Rigorous Empirical Evaluation

arXiv: https://arxiv.org/abs/1808.08504 · HTML: https://mlsanity.com/paper/1808.08504/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2018.

## Built on by (most cited first, 3 of 3)

- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [Event Detection from Social Media Stream: Methods, Datasets and Opportunities](https://mlsanity.com/paper/2306.16495/) (2023)
- [EventNet-ITA: Italian Frame Parsing for Events](https://mlsanity.com/paper/2305.10892/) (2023)

## Abstract (arXiv)

Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts. While the neural network models have generally led the state-of-the-art, the differences in performance between different architectures have not been rigorously studied. In this paper we present a novel GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. We show that it is competitive with other neural network architectures through empirical evaluations under different random initializations and training-validation-test splits of ACE2005 dataset.