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VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling

What it is about

VerbAtlas clusters all 13,767 WordNet verb synsets by hand into 466 frames (EAT, KILL, GIVE, …; about 30 synsets each), each with one prototypical argument structure over 25 cross-frame roles taken from VerbNet (14 rare VerbNet roles merged into coarser ones, e.g. Initial_Location and Initial_State into Source). Each role has selectional preferences given as WordNet hypernyms from a set of 116 synsets (preferences, not restrictions, to allow metaphor). Synsets carry 1,028 implicit-argument labels and 2,979 shadow or default arguments taken from glosses (dine in → Location home; our supermarket delivers → Patient groceries). PropBank framesets and roles of CoNLL-2009 are mapped to VerbAtlas frames, and a BiLSTM SRL model that also predicts the VerbAtlas frame improves slightly on CoNLL-2009 (F1 90.0 in-domain against 89.6 for its base model, with larger gains out of domain).

Ideas

For Mova

13. Verbs (verb meaning), 30-pragmatics-overview.md §5. Code: prag/src/vn.rs, prag/src/vnconv.rs, prag/data/verbnet.tsv, prag/data/nominals.tsv (WordNet already converted there), world/src/knowledge.rs. Measures: frame bench F1 by role, diagnosis step shares, course tests.

Topics: Semantic role labeling

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/d19-1058

@inproceedings{fabio2019verbatlas,
  title = {{VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling}},
  author = {Fabio, Andrea Di and Conia, Simone and Navigli, Roberto},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  year = {2019},
  doi = {10.18653/v1/d19-1058},
  url = {https://doi.org/10.18653/v1/d19-1058}
}

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