@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix orcid: <https://orcid.org/> .
@prefix this: <http://purl.org/np/RAac_pLMhUkhlTNp9Y96JZ7EDAEVTWqmwexucFFz6QP_Q> .
@prefix sub: <http://purl.org/np/RAac_pLMhUkhlTNp9Y96JZ7EDAEVTWqmwexucFFz6QP_Q#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix pav: <http://purl.org/pav/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix doco: <http://purl.org/spar/doco/> .
@prefix c4o: <http://purl.org/spar/c4o/> .

sub:Head {
  this: np:hasAssertion sub:assertion;
    np:hasProvenance sub:provenance;
    np:hasPublicationInfo sub:pubinfo;
    a np:Nanopublication .
}

sub:assertion {
  sub:paragraph c4o:hasContent "Therefore, we adopted a custom frame repository, max- imizing the reuse of the available ones as much as possible, thus serving as a hybrid between FrameNet and Kicktionary. Moreover, we tried to provide a challenging model for the classification task, prioritizing FEs overlap among frames and LU ambiguity (i.e., focusing on very fine-grained semantics with subtle sense differences). We believe this does not only apply to machines, but also to humans: we can view it as a stress test both for the machine learning and the crowdsourcing parts. A total of 6 frames and 15 FEs are modeled with Italian labels as follows:";
    a doco:Paragraph .
}

sub:provenance {
  sub:assertion prov:hadPrimarySource <http://dx.doi.org/10.3233/SW-170269>;
    prov:wasAttributedTo orcid:0000-0002-5456-7964 .
}

sub:pubinfo {
  this: dcterms:created "2019-11-10T18:05:11+01:00"^^xsd:dateTime;
    pav:createdBy orcid:0000-0002-7114-6459 .
}