Dependency-based semantic role labeling of PropBank. Johansson, R. & Nugues, P. Proceedings of the Conference on Empirical Methods in Natural Language Processing EMNLP 08, Association for Computational Linguistics, 2008. ![Dependency-based semantic role labeling of PropBank [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex We present a PropBank semantic role labeling system for English that is integrated with a dependency parser. To tackle the problem of joint syntactic-semantic analysis, the system relies on a syntactic and a semantic subcomponent. The syntactic model is a projective parser using pseudo-projective transformations, and the semantic model uses global inference mechanisms on top of a pipeline of classifiers. The complete syntactic-semantic output is selected from a candidate pool generated by the subsystems. We evaluate the system on the CoNLL-2005 test sets using segment-based and dependency-based metrics. Using the segment-based CoNLL-2005 metric, our system achieves a near state-of-the-art F1 figure of 77.97 on the WSJ+Brown test set, or 78.84 if punctuation is treated consistently. Using a dependency-based metric, the F1 figure of our system is 84.29 on the test set from CoNLL-2008. Our system is the first dependency-based semantic role labeler for PropBank that rivals constituent-based systems in terms of performance.
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abstract = {We present a PropBank semantic role labeling system for English that is integrated with a dependency parser. To tackle the problem of joint syntactic-semantic analysis, the system relies on a syntactic and a semantic subcomponent. The syntactic model is a projective parser using pseudo-projective transformations, and the semantic model uses global inference mechanisms on top of a pipeline of classifiers. The complete syntactic-semantic output is selected from a candidate pool generated by the subsystems. We evaluate the system on the CoNLL-2005 test sets using segment-based and dependency-based metrics. Using the segment-based CoNLL-2005 metric, our system achieves a near state-of-the-art F1 figure of 77.97 on the WSJ+Brown test set, or 78.84 if punctuation is treated consistently. Using a dependency-based metric, the F1 figure of our system is 84.29 on the test set from CoNLL-2008. Our system is the first dependency-based semantic role labeler for PropBank that rivals constituent-based systems in terms of performance.},
bibtype = {article},
author = {Johansson, Richard and Nugues, Pierre},
journal = {Proceedings of the Conference on Empirical Methods in Natural Language Processing EMNLP 08},
number = {October}
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