An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Information Retrieval
SENSE: an analogy-based Word Sense Disambiguation system
Natural Language Engineering
Automatically building conceptual graphs using VerbNet and WordNet
ISICT '04 Proceedings of the 2004 international symposium on Information and communication technologies
Combining contextual features for word sense disambiguation
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Novel semantic features for verb sense disambiguation
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
A supervised algorithm for verb disambiguation into VerbNet classes
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Semantic Role Labeling
High-performance word sense disambiguation with less manual effort
High-performance word sense disambiguation with less manual effort
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
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Subcat-LMF: fleshing out a standardized format for subcategorization frame interoperability
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Verb classification using distributional similarity in syntactic and semantic structures
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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The VerbNet lexical resource classifies English verbs based on semantic and syntactic regularities and has been used for numerous NLP tasks, most notably, semantic role labeling. Since, in addition to thematic roles, it also provides semantic predicates, it can serve as a foundation for further inferencing. Many verbs belong to multiple VerbNet classes, with each class membership corresponding roughly to a different sense of the verb. A VerbNet token classifier is essential for current applications using the resource and could provide the basis for a deep semantic parsing system, one that made full use of VerbNet's extensive syntactic and semantic information. We describe our VerbNet classifier, which uses rich syntactic and semantic features to label verb instances with their appropriate VerbNet class. It achieves an accuracy of 88.67% with multiclass verbs, which is a 49% error reduction over the most frequent class baseline.