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Proceedings of the Ninth Conference on Computational Natural Language Learning - CONLL '05
A maximum entropy classifier is used in our semantic role labeling system, which takes syntactic constituents as the labeling units. The maximum entropy classifier is trained to identify and classify the predicates' semantic arguments together. Only the constituents with the largest probability among embedding ones are kept. After predicting all arguments which have matching constituents in full parsing trees, a simple rule-based post-processing is applied to correct the arguments which have nodoi:10.3115/1706543.1706578 fatcat:ix7p7ewcfbhb7fpqagrhlzokrq