Open Event Extraction from Online Text using a Generative Adversarial Network

By Rui Wang, Deyu Zhou, Yulan He. 2019

46 citations recorded by the source corpus.

To extract the structured representations of open-domain events, Bayesian graphical models have made some progress. However, these approaches typically assume that all words in a document are generated from a single event. While this may be true for short text such as tweets, such an assumption does not generally hold for long text such as news articles. Moreover, Bayesian graphical models often rely on Gibbs sampling for parameter inference which may take long time to converge. To address these limitations, we propose an event extraction model based on Generative Adversarial Nets, called Adversarial-neural Event Model (AEM). AEM models an event with a Dirichlet prior and uses a generator network to capture the patterns underlying latent events. A discriminator is used to distinguish documents reconstructed from the latent events and the original documents. A byproduct of the discriminator is that the features generated by the learned discriminator network allow the visualization of the extracted events. Our model has been evaluated on two Twitter datasets and a news article dataset. Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15% is observed in F-measure.

Subfields: Artificial Intelligence, General Social Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lawrence Carin
2. Jianqiao Li
3. Sijia Wang
4. Yulai Cong
5. Miaoyun Zhao
6. Chunyuan Li
7. Qiang Yang
8. Yu Zhang

In General Social Sciences

1. Lawrence Carin
2. Yulai Cong
3. Miaoyun Zhao

Citations in the corpus

• GAN Memory with No Forgetting (2020, background): “Generative adversarial networks (GANs) GANs have shown increasing power to synthesize highly realistic observations [32, 45, 51, 9, 33, 34], and have found wide applicability in various fields [39, 1, 19, 81, 83, 84, 12, 89, 37].”
• On Leveraging Pretrained GANs for Limited-Data Generation (2020, unknown): “…fields, such as image (Hoffman et al., 2017; Ledig et al., 2017; Ak et al., 2019), text (Lin et al., 2017; Fedus et al., 2018; Wang & Wan, 2018; Wang et al., 2019), video (Mathieu et al., 2015; Wang et al., 2017; 2018a; Chan et al., 2019), and audio (Engel et al., 2019; Yamamoto et al., 2019;…”