Foundations of statistical natural language processing
Foundations of statistical natural language processing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Data Mining
A global joint model for semantic role labeling
Computational Linguistics
Semantic parsing for high-precision semantic role labelling
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Cross-lingual validity of PropBank in the manual annotation of French
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
Co-related verb argument selectional preferences
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Scaling up automatic cross-lingual semantic role annotation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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Semantic role labels are the representation of the grammatically relevant aspects of a sentence meaning. Capturing the nature and the number of semantic roles in a sentence is therefore fundamental to correctly describing the interface between grammar and meaning. In this paper, we compare two annotation schemes, Prop-Bank and VerbNet, in a task-independent, general way, analysing how well they fare in capturing the linguistic generalisations that are known to hold for semantic role labels, and consequently how well they grammaticalise aspects of meaning. We show that VerbNet is more verb-specific and better able to generalise to new semantic role instances, while PropBank better captures some of the structural constraints among roles. We conclude that these two resources should be used together, as they are complementary.