We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information. In contrast to PropBank, which defines enumerative semantic roles, VerbAtlas comes with an explicit, cross-frame set of semantic roles linked to selectional preferences expressed in terms of WordNet synsets, and is the first resource enriched with semantic information about implicit, shadow, and default arguments. We demonstrate the effectiveness of VerbAtlas in the task of dependency-based Semantic Role Labeling and show how its integration into a high-performance system leads to improvements on both the in-domain and out-of-domain test sets of CoNLL-2009. VerbAtlas is available at http://verbatlas.org.
VerbAtlas: a novel large-scale verbal semantic resource and its application to semantic role labeling / DI FABIO, Andrea; Conia, Simone; Navigli, Roberto. - (2019). (Intervento presentato al convegno 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing tenutosi a Hong Kong).
VerbAtlas: a novel large-scale verbal semantic resource and its application to semantic role labeling
andrea di fabio
Primo
Investigation
;CONIA, SIMONESecondo
Software
;roberto navigliUltimo
Conceptualization
2019
Abstract
We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information. In contrast to PropBank, which defines enumerative semantic roles, VerbAtlas comes with an explicit, cross-frame set of semantic roles linked to selectional preferences expressed in terms of WordNet synsets, and is the first resource enriched with semantic information about implicit, shadow, and default arguments. We demonstrate the effectiveness of VerbAtlas in the task of dependency-based Semantic Role Labeling and show how its integration into a high-performance system leads to improvements on both the in-domain and out-of-domain test sets of CoNLL-2009. VerbAtlas is available at http://verbatlas.org.File | Dimensione | Formato | |
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DiFabio_VerbAtlas_2019.pdf
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