Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss

Understand

The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text.

• Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence.
• Previous methods that attempt to address these issues do so with heuristics or with the help of hand-crafted features.
• Instead, we propose an end-to-end solution with a neural network model that uses a variant of cross- entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones.