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# Simone Conia

2 paper cards in Mova's library · 2019–2023

Simone Conia wrote 2 of the papers Mova reads, in 2019–2023, most of them on [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/); often with [Roberto Navigli](https://md.mova.elamur.ai/free/en/docs/papers/authors/roberto-navigli/), Andrea Di Fabio, Riccardo Orlando.

[OpenAlex](https://openalex.org/A5056508457) · [ORCID](https://orcid.org/0000-0002-6238-7816) · [**h-index 12**](https://openalex.org/authors/A5056508457) · [763 citations](https://openalex.org/authors/A5056508457) · [73 works](https://openalex.org/works?filter=author.id:A5056508457) — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Simone+Conia) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Simone+Conia) · [dblp](https://dblp.org/search?q=Simone+Conia) · [ACL Anthology](https://aclanthology.org/search/?q=Simone+Conia) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Simone+Conia)

Topics: [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/) 2

## Papers

1. **[Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities](https://md.mova.elamur.ai/free/en/docs/papers/orlando-2023-exploring-non-verbal-predicates-semantic/)** *(2023)* Most SRL research and benchmarks assume that predicates are verbs. The authors test a state-of-the-art PropBank system (CN-22) trained on OntoNotes. Its argument F1 falls from 84.7 on verbs to 16.4 on nouns, and a noun-trained model falls from 72.8 to 11.2 on verbs, so it does not transfer knowledge across predicate…
2. **[VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling](https://md.mova.elamur.ai/free/en/docs/papers/fabio-2019-verbatlas-novel-large-scale-verbal/)** *(2019)* 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.

## Co-authors (3)

- [Roberto Navigli](https://md.mova.elamur.ai/free/en/docs/papers/authors/roberto-navigli/) ×2
- Andrea Di Fabio
- Riccardo Orlando
- [← previous Simon Mille](https://md.mova.elamur.ai/free/en/docs/papers/authors/simon-mille/)
- [next → Sina Zarrieß](https://md.mova.elamur.ai/free/en/docs/papers/authors/sina-zarries/)
- [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)