Combination Strategies for Semantic Role Labeling
Combination Strategies for Semantic Role Labeling
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Publisher
San Francisco: AI Access Foundation
Journal title
Language
English
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Publisher
San Francisco: AI Access Foundation
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Scope and Contents
Contents
This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and s...
Alternative Titles
Full title
Combination Strategies for Semantic Role Labeling
Authors, Artists and Contributors
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Record Identifier
TN_cdi_proquest_journals_2554119943
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2554119943
Other Identifiers
ISSN
1076-9757
E-ISSN
1076-9757,1943-5037
DOI
10.1613/jair.2088
