Dependency graph

Quantum mechanics

Context (archaeology)

Argument (complex analysis)

Biology

Event (particle physics)

Theoretical computer science

Biochemistry

Data mining

Computer science

Paleontology

Natural language processing

Pooling

Physics

Graph

Pattern recognition (psychology)

Politics

Political science

Artificial intelligence

Representation (politics)

Chemistry

Dependency (UML)

Artificial neural network

Convolutional neural network

Machine learning

Convolution (computer science)

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.

Graph Convolutional Networks With Argument-Aware Pooling for Event Detection