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.
For instance, REF presented a semantic role labeler using convolutional and time-domain neural networks.
Dependency-Based Semantic Role Labeling using Convolutional Neural Networks
*SEM@NAACL-HLT
Computer Science