A survey on deep learning event extraction: approaches and applications

Qian Li, Jianxin Li*, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

Abstract

Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This article fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain EE based on deep learning models. We introduce a new literature classification of current general domain EE research according to the task definition. Afterward, we summarize the paradigm and models of EE approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.

Original languageEnglish
Pages (from-to)6301-6321
Number of pages21
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume35
Issue number5
Early online date21 Oct 2022
DOIs
Publication statusPublished - May 2024

Fingerprint

Dive into the research topics of 'A survey on deep learning event extraction: approaches and applications'. Together they form a unique fingerprint.

Cite this