TY - JOUR
T1 - A survey on deep learning event extraction
T2 - approaches and applications
AU - Li, Qian
AU - Li, Jianxin
AU - Sheng, Jiawei
AU - Cui, Shiyao
AU - Wu, Jia
AU - Hei, Yiming
AU - Peng, Hao
AU - Guo, Shu
AU - Wang, Lihong
AU - Beheshti, Amin
AU - Yu, Philip S.
PY - 2024/5
Y1 - 2024/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85140760989&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2022.3213168
DO - 10.1109/TNNLS.2022.3213168
M3 - Article
C2 - 36269921
SN - 2162-237X
VL - 35
SP - 6301
EP - 6321
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 5
ER -