A striking feature of human syntactic processing is that it is context-dependent, that is, it seems to take into account semantic information from the discourse context and world knowledge. In this paper, we attempt to use this insight to bridge the gap between SRL results from gold parses and from automatically-generated parses. To do this, we jointly perform parsing and semantic role labeling, using a probabilistic SRL system to rerank the results of a probabilistic parser. Our current results are negative, because a locallytrained SRL model can return inaccurate probability estimates.
REF performed joint parsing and semantic role labelling (SRL), using the results of a probabilistic SRL system to improve the accuracy of a probabilistic parser.
Joint Parsing And Semantic Role Labeling
Conference On Computational Natural Language Learning CoNLL
Computer Science