Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowledge to tackle the above challenges. To this end, we propose \emph{Label Reasoning Network(LRN)}, which sequentially reasons fine-grained entity labels by discovering and exploiting label dependencies knowledge entailed in the data. Specifically, LRN utilizes an auto-regressive network to conduct deductive reasoning and a bipartite attribute graph to conduct inductive reasoning between labels, which can effectively model, learn and reason complex label dependencies in a sequence-to-set, end-to-end manner. Experiments show that LRN achieves the state-of-the-art performance on standard ultra fine-grained entity typing benchmarks, and can also resolve the long tail label problem effectively.

翻译：常规实体打字方法基于独立的分类模式,这使得它们难以识别独立、长尾和细细的实体类型。在本文中,我们争辩说,标签之间隐含的外部和内在依赖性可以为解决上述挑战提供关键知识。为此,我们提议采用 emph{标签理由网络(LRN ),这依次是发现和利用数据中包含的标签依赖性知识,从而细化实体标签的缘故。具体地说,LRN利用自动反向网络进行推理和双方属性图,在标签之间进行推理,这可以有效地模拟、学习和解释复杂的标签依赖性,以序列到设定、终端到终端的方式。实验显示,LRN在标准的超细度实体打字基准上达到了最先进的性能,还可以有效解决长尾标签问题。