Modeling Noisy Hierarchical Types in Fine-Grained Entity Typing: A
Content-Based Weighting Approach
Abstract
Fine-grained entity typing (FET), which annotates
the entities in a sentence with a set of finely specified type labels, often serves as the first and critical step towards many natural language processing
tasks. Despite great processes have been made, current FET methods have difficulty to cope with the
noisy labels which naturally come with the data acquisition processes. Existing FET approaches either pre-process to clean the noise or simply focus on one of the noisy labels, sidestepping the fact
that those noises are related and content dependent.
In this paper, we directly model the structured,
noisy labels with a novel content-sensitive weighting schema. Coupled with a newly devised cost
function and a hierarchical type embedding strategy, our method leverages a random walk process
to effectively weight out noisy labels during training. Experiments on several benchmark datasets
validate the effectiveness of the proposed framework and establish it as a new state of the art strategy for noisy entity typing problem