Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations,...) within a document into predefined categories. Correctly identifying these phrases plays a significant role in simplifying information access. However, it remains a difficult task because named entities (NEs) have multiple forms and they are context-dependent. While the context can be represented by contextual features, global relations are often misrepresented by those models. In this paper, we propose the combination of contextual features from XLNet and global features from Graph Convolution Network (GCN) to enhance NER performance. Experiments over a widely-used dataset, CoNLL 2003, show the benefits of our strategy, with results competitive with the state of the art (SOTA).

翻译：命名实体识别(NER)是一种信息提取技术,目的是在文件中将命名实体(例如组织、地点、.)定位和分类为预先界定的类别;正确识别这些短语在简化信息获取方面起着重要作用;然而,由于被命名实体具有多种形式,而且根据具体情况而定,这仍然是一个艰巨的任务;虽然背景特征可以代表这些模型,但全球关系常常被这些模型扭曲;在本文件中,我们提议将XLNet的背景特征与图集集网络的全球特征结合起来,以提高NER的性能;对一个广泛使用的数据集CONLL2003的实验显示了我们战略的效益,其结果与最新技术(SOLTA)具有竞争力。