@prefix dc: <http://purl.org/dc/terms/> .
@prefix this: <http://purl.org/np/RAxKHr1aA8M0wyzPNi_PSVHWr3SspzP40-qCTJhjGHB8c> .
@prefix sub: <http://purl.org/np/RAxKHr1aA8M0wyzPNi_PSVHWr3SspzP40-qCTJhjGHB8c#> .
@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 "Among the top 50 LUs that emerged from the corpus analysis phase, we manually selected a subset of 5 items to facilitate the full implementation of our pipeline. Once the approach has been tested and evaluated, it can scale up to the whole ranking (cf. Section 12 for more observations). The selected LUs comply to two criteria: first, they are picked from both the best and the worst ranked ones, with the purpose of assessing the validity of the corpus analysis as a whole; second, they fit the use case domain, instead of being generic. Consequently, we proceed with the following LUs: esordire (to start out), giocare (to play), perdere (to lose), rimanere (to stay, remain), and vincere (to win)." ;
    a doco:Paragraph .
}
sub:provenance {
  sub:assertion prov:hadPrimarySource <http://dx.doi.org/10.3233/SW-170269> ;
    prov:wasAttributedTo <https://orcid.org/0000-0002-5456-7964> .
}
sub:pubinfo {
  this: dc:created "2019-11-10T18:05:11+01:00"^^xsd:dateTime ;
    pav:createdBy <https://orcid.org/0000-0002-7114-6459> .
}