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Dependency Parsing and Semantic Role Labeling as a Single Task AbstractWe present a comparison between two systems for establishing syntactic and semantic depen- dencies: one that performs dependency parsing and semantic role labeling as a single task, and another that performs the two tasks in isola- tion. The systems are based on local memory- based classiers predicting syntactic and seman- tic dependency relations between pairs of words. In a second global phase, the systems perform a deterministic ranking procedure in which the output of the local classiers is combined per sentence into a dependency graph and seman- tic role labeling assignments for all predicates. The comparison shows that in the learning phase a joint approach produces better-scoring classi- ers, while after the ranking phase the isolated approach produces the most accurate syntactic dependencies, while the joint approach yields the most accurate semantic role assignments.
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