Graph Convolutional Networks With Argument-Aware Pooling for Event Detection

By Thien Huu Nguyen, Ralph Grishman. AAAI Conference on Artificial Intelligence, 2018

377 citations recorded by the source corpus.

The current neural network models for event detection have only considered the sequential representation of sentences. Syntactic representations have not been explored in this area although they provide an effective mechanism to directly link words to their informative context for event detection in the sentences. In this work, we investigate a convolutional neural network based on dependency trees to perform event detection. We propose a novel pooling method that relies on entity mentions to aggregate the convolution vectors. The extensive experiments demonstrate the benefits of the dependency-based convolutional neural networks and the entity mention-based pooling method for event detection. We achieve the state-of-the-art performance on widely used datasets with both perfect and predicted entity mentions.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/af0cf9da53bf3e1e6d349faed14cb68ad71aa2a4

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In Artificial Intelligence

1. Zhiyuan Liu
2. Maosong Sun
3. Chaojun Xiao
4. Kang Liu
5. Cunchao Tu
6. Jian Liu
7. Yubo Chen
8. Haoxiang Zhong

Citations in the corpus

• Graph convolutional networks: a comprehensive review (2019, positive): “Graph convolutional networks have been designed to the relation extraction between words [100, 101] and event extraction [102, 103].”
• Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation (2018, methodology): “To implement modeling with the shortcut arcs, we adopt the graph convolutional networks (GCNs) (Kipf and Welling, 2016; Marcheggiani and Titov, 2017; Nguyen and Grishman, 2018) to learn syntactic contextual representations of each node by the representative vectors of its immediate neighbors in the…”
• Natural Language Processing Advancements By Deep Learning: A Survey (2020, background): “volutional operations are applied to syntactically dependent words as well as consecutive words [117]; their adding entity information reflected the state-of-the-art using CNN models.”
• How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020, positive): “, 2018), and event extraction (Chen et al., 2015; Nguyen et al., 2016; Nguyen and Grishman, 2018).”
• Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection (2019, methodology): “2017) that used supervised attention mechanism to exploit event arguments information for ED; (Sha et al. 2018; Nguyen and Grishman 2018) that incorporated vectorized syntactic knowledge and others.”