Paper: Discriminative vs. Generative Approaches in Semantic Role Labeling

ACL ID W08-2131
Title Discriminative vs. Generative Approaches in Semantic Role Labeling
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2008
Authors

This paper describes the two algorithms we developed for the CoNLL 2008 Shared Task “Joint learning of syntactic and se- mantic dependencies”. Both algorithms start parsing the sentence using the same syntactic parser. The first algorithm uses machine learning methods to identify the semantic dependencies in four stages: identification and labeling of predicates, identification and labeling of arguments. The second algorithm uses a generative probabilistic model, choosing the seman- tic dependencies that maximize the proba- bility with respect to the model. A hybrid algorithm combining the best stages of the two algorithms attains 86.62% labeled syntactic attachment accuracy, 73.24% la- beled semantic dependency F1 and 79.93% labeled macro F1 score for the combined WSJ and Brown test s...