Paper: DeSRL: A Linear-Time Semantic Role Labeling System

ACL ID W08-2138
Title DeSRL: A Linear-Time Semantic Role Labeling System
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2008
Authors

This paper describes the DeSRL sys- tem, a joined effort of Yahoo! Research Barcelona and Universit`a di Pisa for the CoNLL-2008 Shared Task (Surdeanu et al., 2008). The system is characterized by an efficient pipeline of linear complexity components, each carrying out a different sub-task. Classifier errors and ambigui- ties are addressed with several strategies: revision models, voting, and reranking. The system participated in the closed chal- lenge ranking third in the complete prob- lem evaluation with the following scores: 82.06 labeled macro F1 for the overall task, 86.6 labeled attachment for syntactic de- pendencies, and 77.5 labeled F1 for se- mantic dependencies. 1 System description DeSRL is implemented as a sequence of compo- nents of linear complexity relative to the sentence...