Discriminative vs. Generative Approaches in Semantic Role Labeling

Deniz Yuret, Mehmet Ali Yatbaz and Ahmet Engin Ural. In Proceedings of The Twelfth Conference on Natural Language Learning (CoNLL-2008) (PDF, ACM)

Abstract: This paper describes the two algorithms we developed for the CoNLL 2008 Shared Task “Joint learning of syntactic and semantic 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 semantic dependencies that maximize the probability 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% labeled semantic dependency F1 and 79.93% labeled macro F1 score for the combined WSJ and Brown test sets.

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