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        <title>Classifying Verbs in WordNet by Harnessing Semantic Resources</title>
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    <abstract>This paper presents the principles and procedures involved in the construction of a classification of verbs using information from 3 semantic resources – WordNet, FrameNet and VerbNet. We adopt the FrameNet frames as the primary categories of the proposed classification and transfer them to WordNet synsets. The hierarchical relationships between the categories are projected both from the hypernymy relation in WordNet and from the hierarchy of some of the frame-to-frame relations in FrameNet. The semantic classes and their hierarchical organisation in WordNet are thus made explicit and allow for linguistic generalisations on the inheritance of semantic features and structures. We then select the beginners of the separate hierarchies and assign classification categories recursively to their hyponyms using a battery of procedures based on generalisations over the semantic primes and the hierarchical structure of WordNet and FrameNet and correspondences between VerbNet superclasses and FrameNet frames. The so-obtained suggestions are ranked according to probability. As a result, 13,465 out of 14,206 verb synsets are accommodated in the classification hierarchy at least through a general category, which provides a point of departure towards further refinement of categories. The resulting system of classification categories is initially derived from the WordNet hierarchy and is further validated against the hierarchy of frames within FrameNet. A set of procedures is established to address inconsistencies and heterogeneity of categories. The classification is subject to ongoing extensive manual verification, essential for ensuring the quality of the resource.</abstract>
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%0 Conference Proceedings
%T Classifying Verbs in WordNet by Harnessing Semantic Resources
%A Leseva, Svetlozara
%A Stoyanova, Ivelina
%A Todorova, Maria
%S Proceedings of the Third International Conference on Computational Linguistics in Bulgaria (CLIB 2018)
%D 2018
%8 May
%I Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences
%C Sofia, Bulgaria
%F leseva-etal-2018-classifying
%X This paper presents the principles and procedures involved in the construction of a classification of verbs using information from 3 semantic resources – WordNet, FrameNet and VerbNet. We adopt the FrameNet frames as the primary categories of the proposed classification and transfer them to WordNet synsets. The hierarchical relationships between the categories are projected both from the hypernymy relation in WordNet and from the hierarchy of some of the frame-to-frame relations in FrameNet. The semantic classes and their hierarchical organisation in WordNet are thus made explicit and allow for linguistic generalisations on the inheritance of semantic features and structures. We then select the beginners of the separate hierarchies and assign classification categories recursively to their hyponyms using a battery of procedures based on generalisations over the semantic primes and the hierarchical structure of WordNet and FrameNet and correspondences between VerbNet superclasses and FrameNet frames. The so-obtained suggestions are ranked according to probability. As a result, 13,465 out of 14,206 verb synsets are accommodated in the classification hierarchy at least through a general category, which provides a point of departure towards further refinement of categories. The resulting system of classification categories is initially derived from the WordNet hierarchy and is further validated against the hierarchy of frames within FrameNet. A set of procedures is established to address inconsistencies and heterogeneity of categories. The classification is subject to ongoing extensive manual verification, essential for ensuring the quality of the resource.
%U https://aclanthology.org/2018.clib-1.15
%P 115-125

Markdown (Informal)

[Classifying Verbs in WordNet by Harnessing Semantic Resources](https://aclanthology.org/2018.clib-1.15) (Leseva et al., CLIB 2018)

• Classifying Verbs in WordNet by Harnessing Semantic Resources (Leseva et al., CLIB 2018)

ACL

• Svetlozara Leseva, Ivelina Stoyanova, and Maria Todorova. 2018. Classifying Verbs in WordNet by Harnessing Semantic Resources. In Proceedings of the Third International Conference on Computational Linguistics in Bulgaria (CLIB 2018), pages 115–125, Sofia, Bulgaria. Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences.