Difference between revisions of "Palmer et al Computational Linguistics 2005"
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The [[Category::paper]] presents how they built The Proposition Bank ([[PropBank]]) corpus. In addition, the paper describes an automatic system for [[AddressesProblem::Semantic Role Labeling]] trained on the corpus. | The [[Category::paper]] presents how they built The Proposition Bank ([[PropBank]]) corpus. In addition, the paper describes an automatic system for [[AddressesProblem::Semantic Role Labeling]] trained on the corpus. | ||
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| + | For automatic determination of semantic role labels, they adopted the features and probability model of [[Gildea and Jurafsky Computational Linguistics 2002]] for their initial experiments. While [[Gildea and Jurafsky Computational Linguistics 2002]] do not have a gold standard of parse tree, they do have a gold standard of parse trees, and they show improvements in the performance of the system. | ||
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| + | Features used for the system are the phrase type, the parse tree path, the position, the voice, and the head word. | ||
== Key Contribution == | == Key Contribution == | ||
This system is the first statistical model on FrameNet solving the semantic role labeling problem, and future systems use the features introduced in this paper as a baseline. This paper is also very worth to read in that it describes the whole process of semantic role labeling in detail. In addition, they did many various experiments to find out which features, algorithms, and techniques affect the performance of the system. | This system is the first statistical model on FrameNet solving the semantic role labeling problem, and future systems use the features introduced in this paper as a baseline. This paper is also very worth to read in that it describes the whole process of semantic role labeling in detail. In addition, they did many various experiments to find out which features, algorithms, and techniques affect the performance of the system. | ||
Revision as of 02:15, 29 November 2010
Citation
Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005. The Proposition Bank: An Annotated Corpus of Semantic Roles. Computational Linguistics, 31(1):71–106.
Online version
Summary
The paper presents how they built The Proposition Bank (PropBank) corpus. In addition, the paper describes an automatic system for Semantic Role Labeling trained on the corpus.
For automatic determination of semantic role labels, they adopted the features and probability model of Gildea and Jurafsky Computational Linguistics 2002 for their initial experiments. While Gildea and Jurafsky Computational Linguistics 2002 do not have a gold standard of parse tree, they do have a gold standard of parse trees, and they show improvements in the performance of the system.
Features used for the system are the phrase type, the parse tree path, the position, the voice, and the head word.
Key Contribution
This system is the first statistical model on FrameNet solving the semantic role labeling problem, and future systems use the features introduced in this paper as a baseline. This paper is also very worth to read in that it describes the whole process of semantic role labeling in detail. In addition, they did many various experiments to find out which features, algorithms, and techniques affect the performance of the system.
