Abstract
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.
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Notes
- 1.
This expanded corpus is now available at https://verbs.colorado.edu/wiki/index.php/Main_Page.
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Brown, S.W., Dligach, D., Palmer, M. (2014). VerbNet Class Assignment as a WSD Task. In: Bunt, H., Bos, J., Pulman, S. (eds) Computing Meaning. Text, Speech and Language Technology, vol 47. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7284-7_11
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