Fine-grained Entity Typing via Label Reasoning
This work addresses the challenge of fine-grained entity typing for natural language processing applications, offering a novel method that improves accuracy on standard benchmarks.
The paper tackles the problem of fine-grained entity typing by addressing interdependent, long-tailed, and fine-grained types, proposing a Label Reasoning Network (LRN) that uses deductive and inductive reasoning to model label dependencies, achieving state-of-the-art performance on benchmarks and effectively resolving long-tail label issues.
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.