Dependency-Based Semantic Role Labeling using Convolutional Neural Networks
By William Foland, James H. Martin. 2015
39 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Management Science and Operations Research
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In Artificial Intelligence
- Diego Marcheggiani
- Ivan Titov
- William L. Hamilton
- Nicola De Cao
- Xavier Bresson
- Thomas Laurent
- Junlin Yang
- Shay B. Cohen
In Management Science and Operations Research
Citations in the corpus
- Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling (2017, background): “Also Foland and Martin (2015) used (non-graph) convolutional networks and provided syntactic features as input.”