Automatic labeling of semantic roles
Computational Linguistics
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Frame Detection over the Semantic Web
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
New features for FrameNet: WordNet mapping
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
A semantic approach to textual entailment: system evaluation and task analysis
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Kernel methods for minimally supervised wsd
Computational Linguistics
BabelNet: building a very large multilingual semantic network
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
SemEval-2010 task 10: Linking events and their participants in discourse
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Gathering lexical linked data and knowledge patterns from FrameNet
Proceedings of the sixth international conference on Knowledge capture
Putting pieces together: combining FrameNet, VerbNet and WordNet for robust semantic parsing
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Wikipedia-based WSD for multilingual frame annotation
Artificial Intelligence
Investigating the semantics of frame elements
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
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FrameNet is a large-scale lexical resource encoding information about semantic frames (situations) and semantic roles. The aim of the paper is to enrich FrameNet by mapping the lexical fillers of semantic roles to WordNet using a Wikipedia-based detour. The applied methodology relies on a word sense disambiguation step, in which a Wikipedia page is assigned to a role filler, and then BabelNet and YAGO are used to acquire WordNet synsets for a filler. We show how to represent the acquired resource in OWL, linking it to the existing RDF/OWL representations of FrameNet and WordNet. Part of the resource is evaluated by matching it with the WordNet synsets manually assigned by FrameNet lexicographers to a subset of semantic roles.