  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.
