Matches in Nanopublications for { ?s ?p ?o <http://purl.org/np/RArc_OOsBqt-3qfR_EKZywvfy4E-YJ0Ac2tTrD57aIGnI#assertion>. }
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- paragraph type Paragraph assertion.
- paragraph hasContent "Both frame and FEs recognition are cast to a multi- class classification task: while the former can be related to text categorization, the latter should answer questions such as can this entity be this FE? or is this entity this FE in this context?. Such activity boils down to semantic role labeling (cf. [24] for an introduction), and usually requires a more fine-grained text analysis. Previous work in the area exploits deeper NLP layers, such as syntactic parsing (e.g., [25]). We alleviate this through EL techniques, which perform word sense dis- ambiguation by linking relevant parts of a source sentence to URIs of a target KB. We leverage THE WIKI MACHINE 17 [19], a state-of-the-art [26] approach conceived for connecting text to Wikipedia URLs, thus inherently entailing DBpedia URIs. EL results are part of the FE classifier feature set. We claim that EL enables the automatic addition of features based on existing entity attributes within the target KB (notably, the class of an entity, which represents its semantic type)." assertion.