Paper: Dependency Tree-based SRL with Proper Pruning and Extensive Feature Engineering

ACL ID W08-2137
Title Dependency Tree-based SRL with Proper Pruning and Extensive Feature Engineering
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2008
Authors

This paper proposes a dependency tree- based SRL system with proper pruning and extensive feature engineering. Official evaluation on the CoNLL 2008 shared task shows that our system achieves 76.19 in la- beled macro F1 for the overall task, 84.56 in labeled attachment score for syntactic dependencies, and 67.12 in labeled F1 for semantic dependencies on combined test set, using the standalone MaltParser. Be- sides, this paper also presents our unofficial system by 1) applying a new effective pruning algorithm; 2) including additional features; and 3) adopting a better depend- ency parser, MSTParser. Unofficial evalua- tion on the shared task shows that our sys- tem achieves 82.53 in labeled macro F1, 86.39 in labeled attachment score, and 78.64 in labeled F1, using MSTParser ...