Papers with VerbAtlas

3 papers
InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles (2020.emnlp-demos)

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Challenge: Semantic Role Labeling (SRL) is dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts.
Approach: They propose a platform for semantic role labeling that provides verb sense and semantic role information with an easy to use Web interface and RESTful APIs.
Outcome: The proposed system provides human-readable verb sense and semantic role information with an easy to use Web interface and RESTful APIs.
VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling (D19-1)

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Challenge: VerbAtlas is a lexical-semantic resource that combines WordNet synsets into semantically-coherent frames.
Approach: They propose a lexical-semantic resource that brings together WordNet synsets into semantically-coherent frames.
Outcome: The proposed resource brings together all WordNet synsets into semantically-coherent frames.
Fully-Semantic Parsing and Generation: the BabelNet Meaning Representation (2022.acl-long)

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Challenge: Abstract Meaning Representation (AMR) is the most popular formalism for Semantic Parsing.
Approach: They propose a language-independent representation of meaning using BabelNet and VerbAtlas.
Outcome: The proposed framework outperforms existing frameworks thanks to fully-semantic framing, the authors show . the proposed dataset is labeled entirely according to the proposed framework, and is available on github.

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