Improving Event Extraction via Multimodal Integration

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Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection (2022, background): “Lifelong Event Detection Deep neural networks have shown state-of-the-art performance on supervised event detection (Nguyen et al., 2016; Feng et al., 2016; Zhang et al., 2017; Huang and Ji, 2020; Wang et al., 2021b).”
Multi-Modal Knowledge Graph Construction and Application: A Survey (2022, methodology): “Apriori algorithm) can be utilized to mine frequent visual image patches to find association rules for predicting the event type by visual patterns [92], [97].”
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2021, background): “Traditional event extraction studies (McClosky et al., 2011; Li et al., 2013; Chen et al., 2015; Cao et al., 2015; Feng et al., 2016; Yang and Mitchell, 2016; Nguyen et al., 2016; Zhang et al., 2017; Wadden et al., 2019; Lin et al., 2020; Wang et al., 2021) usually detect event triggers and argument”
The Art of Prompting: Event Detection based on Type Specific Prompts (2022, methodology): “…Liao and Grishman, 2010; McClosky et al., 2011; Li et al., 2013; Chen et al., 2015; Cao et al., 2015; Feng et al., 2016; Yang and Mitchell, 2016; Nguyen et al., 2016; Zhang et al., 2017; Lin et al., 2020; Wang et al., 2021b), however, they cannot be directly applied to detect new types of events.”