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 locally-trained SRL model can return inaccurate probability estimates.
|Title of host publication||Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)|
|Place of Publication||Ann Arbor, Michigan|
|Publisher||Association for Computational Linguistics|
|Number of pages||4|
|Publication status||Published - 1 Jun 2005|