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

TL;DR

This paper introduces a neural network approach for fine-grained entity type classification that effectively handles noisy labels and hierarchical structures, outperforming previous methods on standard benchmarks.

Contribution

The authors propose a novel end-to-end neural model with hierarchy-aware loss functions that improves robustness to noisy labels and simplifies the classification process.

Findings

Outperforms state-of-the-art on benchmark datasets.

Robust against noisy and overly-specific labels.

Uses hierarchy-aware loss normalization effectively.

Abstract

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. 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-the-art on established benchmarks for the task.