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Proceedings of the Twelfth Conference on Computational Natural Language Learning - CoNLL '08
In this paper, we report experiments that explore learning of syntactic and semantic representations. First, we extend a state-of-the-art statistical parser to produce a richly annotated tree that identifies and labels nodes with semantic role labels as well as syntactic labels. Secondly, we explore rule-based and learning techniques to extract predicate-argument structures from this enriched output. The learning method is competitive with previous single-system proposals for semantic roledoi:10.3115/1596324.1596326 fatcat:locby5ogebgk3mp6lxr34ehnke