Deep Learning Schema-based Event Extraction: Literature Review and Current Trends

• URL: http://arxiv.org/abs/2107.02126v2
• Date: Tue, 6 Jul 2021 07:25:06 GMT
• Title: Deep Learning Schema-based Event Extraction: Literature Review and Current Trends
• Authors: Qian Li, Hao Peng, Jianxin Li, Yiming Hei, Rui Sun, Jiawei Sheng, Shu Guo, Lihong Wang, Philip S. Yu
• Abstract summary: Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
• Score: 60.29289298349322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Schema-based 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 schema-based 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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