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.

翻译：在本文中,我们调查了依赖树在使用一套GCN时在名称实体识别器中的作用。我们比较了不同的NER结构,并表明句子的语法对结果产生了积极影响。 上注数据集实验表明业绩不断改善,而不需要重型地物工程或额外的语言知识。