Dependency-Based Semantic Role Labeling using Convolutional Neural Networks
William Foland, James Martin · 2015
We describe a semantic role labeler with stateof-the-art performance and low computational requirements, which uses convolutional and time-domain neural networks.The system is designed to work with features derived from a dependency parser output.Various system options and architectural details are discussed.Incremental experiments were run to explore the benefits of adding increasingly more complex dependency-based features to the system; results are presented for both in-domain and out-of-domain datasets.