Deep Learning Schema-based Event Extraction: Literature Review and Current Trends.
Year: 2021
Type: preprint
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.
Source: arXiv (Cornell University)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3181438400