Kavli Affiliate: Lihong Wang
| First 5 Authors: Qian Li, Jianxin Li, Jiawei Sheng, Shiyao Cui, Jia Wu
| Summary:
Event extraction is a critical technique to apprehend the essential content
of events promptly. With the rapid development of deep learning technology,
event extraction technology based on deep learning 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 paper fills the gap by reviewing the state-of-the-art approaches, focusing
on deep learning-based models. We summarize the task definition, paradigm, and
models of event extraction and then discuss each of these in detail. We
introduce benchmark datasets that support tests of predictions and evaluation
metrics. A comprehensive comparison between different techniques is also
provided in this survey. Finally, we conclude by summarizing future research
directions facing the research area.
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