PMID- 36269921
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240504
IS  - 2162-2388 (Electronic)
IS  - 2162-237X (Linking)
VI  - 35
IP  - 5
DP  - 2024 May
TI  - A Survey on Deep Learning Event Extraction: Approaches and Applications.
PG  - 6301-6321
LID - 10.1109/TNNLS.2022.3213168 [doi]
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.
FAU - Li, Qian
AU  - Li Q
FAU - Li, Jianxin
AU  - Li J
FAU - Sheng, Jiawei
AU  - Sheng J
FAU - Cui, Shiyao
AU  - Cui S
FAU - Wu, Jia
AU  - Wu J
FAU - Hei, Yiming
AU  - Hei Y
FAU - Peng, Hao
AU  - Peng H
FAU - Guo, Shu
AU  - Guo S
FAU - Wang, Lihong
AU  - Wang L
FAU - Beheshti, Amin
AU  - Beheshti A
FAU - Yu, Philip S
AU  - Yu PS
LA  - eng
PT  - Journal Article
DEP - 20240502
PL  - United States
TA  - IEEE Trans Neural Netw Learn Syst
JT  - IEEE transactions on neural networks and learning systems
JID - 101616214
SB  - IM
EDAT- 2022/10/22 06:00
MHDA- 2022/10/22 06:01
CRDT- 2022/10/21 16:03
PHST- 2022/10/22 06:01 [medline]
PHST- 2022/10/22 06:00 [pubmed]
PHST- 2022/10/21 16:03 [entrez]
AID - 10.1109/TNNLS.2022.3213168 [doi]
PST - ppublish
SO  - IEEE Trans Neural Netw Learn Syst. 2024 May;35(5):6301-6321. doi: 
      10.1109/TNNLS.2022.3213168. Epub 2024 May 2.