@prefix dc: . @prefix this: . @prefix sub: . @prefix xsd: . @prefix prov: . @prefix pav: . @prefix np: . @prefix linkflows: . sub:Head { this: np:hasAssertion sub:assertion; np:hasProvenance sub:provenance; np:hasPublicationInfo sub:pubinfo; a np:Nanopublication . } sub:assertion { sub:comment-12 a linkflows:ActionNeededComment, linkflows:ContentComment, linkflows:NegativeComment, linkflows:ReviewComment; linkflows:hasCommentText "* While it is certainly fair to say that the workflow as proposed in the paper makes use of a \"lightweight NLP machinery\" only, the NLP pipeline still requires a lot of manual effort due to the construction of domain-specific FrameNets and the manual annotation work that is needed in order to train classifiers for frame and frame element detection. These modules being core parts of the pipeline, it is certainly not adequate to claim that there be \"no need for ... semantic role labeling\" in FactExtractor."; linkflows:hasImpact "3"^^xsd:positiveInteger; linkflows:refersTo . } sub:provenance { sub:assertion prov:hadPrimarySource ; prov:wasAttributedTo . } sub:pubinfo { this: dc:created "2019-11-26T09:05:11+01:00"^^xsd:dateTime; pav:createdBy . }