ArticleData science and engineering2023
Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event.
Article in Data science and engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Robotic Tactile Sensing for Early Detection of Frost-Damaged Citrus Fruits with Pressure-Vibration Multimodal Fusion.Foods (Basel, Switzerland) · 2026Article
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5 authors.
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Abstract
In recent years, Coronavirus disease 2019 (COVID-19) has become a global epidemic, and some efforts have been devoted to tracking and controlling its spread. Extracting structured knowledge from involved epidemic case reports can inform the surveillance system, which is important for controlling the spread of outbreaks. Therefore, in this paper, we focus on the task of Chinese epidemic event extraction (EE), which is defined as the detection of epidemic-related events and corresponding arguments in the texts of epidemic case reports. To facilitate the research of this task, we first define the epidemic-related event types and argument roles. Then we manually annotate a Chinese COVID-19 epidemic dataset, named COVID-19 Case Report (CCR). We also propose a novel hierarchical EE architecture, named Supplementary Information: The online version contains supplementary material available at 10.1007/s41019-022-00203-6.
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