Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss
Peng Xu, Denilson Barbosa · 2018
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 crossentropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones.Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing.In contrast, our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context.We show experimentally that our approach is robust against noise and consistently outperforms the state-of-theart on established benchmarks for the task.