  In this paper we investigate the role of the dependency tree in a named
entity recognizer upon using a set of GCN. We perform a comparison among
different NER architectures and show that the grammar of a sentence positively
influences the results. Experiments on the ontonotes dataset demonstrate
consistent performance improvements, without requiring heavy feature
engineering nor additional language-specific knowledge.
